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Last update:Friday 16th of December 2011

(1) Unit 0w

(15) Unit 1w

(02:00) 1 Introduction

(01:34) 2 Intelligent Agents

(06:28) 3 Applications of AI

So the correct answer is all of those--	▶ 00:00
finance, robotics, games, medicine, the Web, and many more applications.	▶ 00:03
So let me talk about them in some detail.	▶ 00:08
There is a huge number of applications of artificial intelligence in finance,	▶ 00:10
very often in the shape of making trading decisions--	▶ 00:15
in which case, the agent is called a trading agent.	▶ 00:18
And the environment might be things like the stock market or the bond market	▶ 00:21
or the commodities market.	▶ 00:27
And our trading agent can sense the course of certain things,	▶ 00:29
like the stock or bonds or commodities.	▶ 00:33
It can also read the news online and follow certain events.	▶ 00:35
And its decisions are usually things like buy or sell decisions--trades.	▶ 00:40
There's a huge history of artificial intelligence finding methods to look at data over time	▶ 00:48
and make predictions as to how courses develop over time--	▶ 00:55
and then put in trades behind those.	▶ 00:58
And very frequently, people using artificial intelligence trading agents	▶ 01:01
have made a good amount of money with superior trading decisions.	▶ 01:06
There's also a long history of AI in Robotics.	▶ 01:10
Here is my depiction of a robot.	▶ 01:14
Of course, there are many different types of robots	▶ 01:17
and they all interact with their environments through their sensors,	▶ 01:20
which include things like cameras, microphones, tactile sensor or touch.	▶ 01:24
And the way they impact their environments is to move motors around,	▶ 01:33
in particular, their wheels, their legs, their arms, their grippers.	▶ 01:38
They can also say things to people using voice.	▶ 01:43
Now there's a huge history of using artificial intelligence in robotics.	▶ 01:46
Pretty much, every robot that does something interesting today uses AI.	▶ 01:50
In fact, often AI has been studied together with robotics, as one discipline.	▶ 01:54
But because robots are somewhat special in that they use physical actuators	▶ 01:58
and deal with physical environments, they are a little bit different from	▶ 02:03
just artificial intelligence, as a whole.	▶ 02:06
When the Web came out, the early Web crawlers were called robots	▶ 02:08
and to block a robot from accessing your website, to the present day,	▶ 02:15
there's a file called robot.txt, that allows you to deny any Web crawler	▶ 02:20
to access and retrieve that information from your website.	▶ 02:24
So historically, robotics played a huge role in artificial intelligence	▶ 02:28
and a good chunk of this class will be focusing on robotics.	▶ 02:32
AI has a huge history in games--	▶ 02:36
to make games smarter or feel more natural.	▶ 02:39
There are 2 ways in which AI has been used in games, as a game agent.	▶ 02:43
One is to play against you, as a human user.	▶ 02:47
So for example, if you play the game of Chess,	▶ 02:50
then you are the environment to the game agent.	▶ 02:54
The game agent gets to observe your moves, and it generates its own moves	▶ 02:57
with the purpose of defeating you in Chess.	▶ 03:03
So most adversarial games, where you play against an opponent	▶ 03:07
and the opponent is a computer program,	▶ 03:10
the game agent is built to play against you--against your own interests--and make you lose.	▶ 03:13
And of course, your objective is to win.	▶ 03:20
That's an AI games-type situation.	▶ 03:22
The second thing is that games agents in AI	▶ 03:25
also are used to make games feel more natural.	▶ 03:29
So very often games have characters inside, and these characters act in some way.	▶ 03:32
And it's important for you, as the player, to feel that these characters are believable.	▶ 03:36
There's an entire sub-field of artificial intelligence to use AI	▶ 03:42
to make characters in a game more believable--look smarter, so to speak--	▶ 03:45
so that you, as a player, think you're playing a better game.	▶ 03:51
Artificial intelligence has a long history in medicine as well.	▶ 03:55
The classic example is that of a diagnostic agent.	▶ 04:00
So here you are--and you might be sick, and you go to your doctor.	▶ 04:04
And your doctor wishes to understand	▶ 04:09
what the reason for your symptoms and your sickness is.	▶ 04:11
The diagnostic agent will observe you through various measurements--	▶ 04:17
for example, blood pressure and heart signals, and so on--	▶ 04:21
and it'll come up with the hypothesis as to what you might be suffering from.	▶ 04:25
But rather than intervene directly, in most cases the diagnostic of your disease	▶ 04:29
is communicated to the doctor, who then takes on the intervention.	▶ 04:34
This is called a diagnostic agent.	▶ 04:38
There are many other versions of AI in medicine.	▶ 04:40
AI is used in intensive care to understand whether there are situations	▶ 04:43
that need immediate attention.	▶ 04:48
It's been used for life-long medicine to monitor signs over long periods of time.	▶ 04:50
And as medicine becomes more personal, the role of artificial intelligence	▶ 04:54
will definitely increase.	▶ 04:58
We already mentioned AI on the Web.	▶ 05:01
The most generic version of AI is to crawl the Web and understand the Web,	▶ 05:05
and assist you in answering questions.	▶ 05:09
So when you have this search box over here	▶ 05:12
and it says "Search" on the left,	▶ 05:15
and "I'm Feeling Lucky" on the right,	▶ 05:18
and you type in the words,	▶ 05:20
what AI does for you is it understands what words you typed in	▶ 05:21
and finds the most relevant pages.	▶ 05:28
That is really co-artificial intelligence.	▶ 05:30
It's used by a number of companies, such as Microsoft and Google	▶ 05:32
and Amazon, Yahoo, and many others.	▶ 05:36
And the way this works is that there's a crawling agent that can go	▶ 05:39
to the World Wide Web and retrieve pages, through just a computer program.	▶ 05:43
It then sorts these pages into a big database inside the crawler	▶ 05:51
and also analyzes developments of each page to any possible query.	▶ 05:56
When you then come and issue a query,	▶ 06:01
the AI system is able to give you a response--	▶ 06:04
for example, a collection of 10 best Web links.	▶ 06:08
In short, every time you try to write a piece of software,	▶ 06:12
that makes your computer software smart	▶ 06:15
likely you will need artificial intelligence.	▶ 06:18
And in this class, Peter and I will teach you	▶ 06:20
many of the basic tricks of the trade	▶ 06:23
to make your software really smart.	▶ 06:25

(05:17) 4 Terminology

It will be good to introduce some basic terminology	▶ 00:00
that is commonly used in artificial intelligence to distinguish different types of problems.	▶ 00:04
The very first word I will teach you is fully versus partially observable.	▶ 00:09
An environment is called fully observable if what your agent can sense	▶ 00:16
at any point in time is completely sufficient to make the optimal decision.	▶ 00:19
So, for example, in many card games,	▶ 00:26
when all the cards are on the table, the momentary site of all those cards	▶ 00:29
is really sufficient to make the optimal choice.	▶ 00:36
That is in contrast to some other environments where you need memory	▶ 00:40
on the side of the agent to make the best possible decision.	▶ 00:46
For example, in the game of poker, the cards aren't openly on the table,	▶ 00:50
and memorizing past moves will help you make a better decision.	▶ 00:55
To fully understand the difference, consider the interaction of an agent	▶ 01:00
with the environment to its sensors and its actuators,	▶ 01:04
and this interaction takes place over many cycles,	▶ 01:08
often called the perception-action cycle.	▶ 01:11
For many environments, it's convenient to assume	▶ 01:16
that the environment has some sort of internal state.	▶ 01:19
For example, in a card game where the cards are not openly on the table,	▶ 01:22
the state might pertain to the cards in your hand.	▶ 01:28
An environment is fully observable if the sensors can always see	▶ 01:33
the entire state of the environment.	▶ 01:37
It's partially observable if the sensors can only see a fraction of the state,	▶ 01:41
yet memorizing past measurements gives us additional information of the state	▶ 01:46
that is not readily observable right now.	▶ 01:52
So any game, for example, where past moves have information about	▶ 01:55
what might be in a person's hand, those games are partially observable,	▶ 02:01
and they require different treatment.	▶ 02:06
Very often agents that deal with partially observable environments	▶ 02:08
need to acquire internal memory to understand what	▶ 02:12
the state of the environment is, and we'll talk extensively	▶ 02:15
when we talk about hidden Markov models about how this structure	▶ 02:18
has such internal memory.	▶ 02:21
A second terminology for environments pertains to whether the environment	▶ 02:23
is deterministic or stochastic.	▶ 02:26
Deterministic environment is one where your agent's actions	▶ 02:29
uniquely determine the outcome.	▶ 02:35
So, for example, in chess, there's really no randomness when you move a piece.	▶ 02:37
The effect of moving a piece is completely predetermined,	▶ 02:42
and no matter where I'm going to move the same piece, the outcome is the same.	▶ 02:46
That we call deterministic.	▶ 02:50
Games with dice, for example, like backgammon, are stochastic.	▶ 02:52
While you can still deterministically move your pieces,	▶ 02:56
the outcome of an action also involves throwing of the dice,	▶ 03:00
and you can't predict those.	▶ 03:03
There's a certain amount of randomness involved for the outcome of dice,	▶ 03:05
and therefore, we call this stochastic.	▶ 03:08
Let me talk about discrete versus continuous.	▶ 03:10
A discrete environment is one where you have finitely many action choices,	▶ 03:14
and finitely many things you can sense.	▶ 03:18
So, for example, in chess, again, there's finitely many board positions,	▶ 03:21
and finitely many things you can do.	▶ 03:25
That is different from a continuous environment	▶ 03:28
where the space of possible actions or things you could sense may be infinite.	▶ 03:30
So, for example, if you throw darts, there's infinitely many ways to angle the darts	▶ 03:35
and to accelerate them.	▶ 03:41
Finally, we distinguish benign versus adversarial environments.	▶ 03:43
In benign environments, the environment might be random.	▶ 03:49
It might be stochastic, but it has no objective on its own	▶ 03:53
that would contradict the own objective.	▶ 03:57
So, for example, weather is benign.	▶ 03:59
It might be random. It might affect the outcome of your actions.	▶ 04:02
But it isn't really out there to get you.	▶ 04:06
Contrast this with adversarial environments, such as many games, like chess,	▶ 04:08
where your opponent is really out there to get you.	▶ 04:14
It turns out it's much harder to find good actions in adversarial environments	▶ 04:16
where the opponent actively observes you and counteracts what you're trying to achieve	▶ 04:21
relative to benign environment, where the environment might merely be stochastic	▶ 04:26
but isn't really interested in making your life worse.	▶ 04:30
So, let's see to what extent these expressions make sense to you	▶ 04:35
by going to our next quiz.	▶ 04:38
So here are the 4 concepts again: partially observable versus fully,	▶ 04:40
stochastic versus deterministic, continuous versus discrete,	▶ 04:45
adversarial versus benign.	▶ 04:50
And let me ask you about the game of checkers.	▶ 04:52
Check one or all of those attributes that apply.	▶ 04:56
So, if you think checkers is partially observable, check this one.	▶ 05:00
Otherwise, just don't check it.	▶ 05:03
If you think it's stochastic, check this one,	▶ 05:05
continuous, check this one, adversarial, check this one.	▶ 05:07
If you don't know about checkers, you can check the Web and Google it	▶ 05:11
to find a little more information about checkers.	▶ 05:15

(00:52) 5 Checkers Answer

(00:12) 6 Poker
[Male narrator] The game of poker--is this partially observable, stochastic, ▶ 00:00
continuous, or adversarial? ▶ 00:06
Please check any or all of those that apply. ▶ 00:09

(00:30) 7 Poker Answer

(00:22) 8 Robotic Car

(00:33) 9 Robotic Car Answer

(01:28) 10 AI and Uncertainty

(04:00) 11 Examples of AI in Practice

Now we've had an introduction to AI.	▶ 00:00
We've heard about some of the properties of environments,	▶ 00:03
and we've seen some possible architecture for agents.	▶ 00:06
I'd like next to show you some examples of AI in practice.	▶ 00:10
And Sebastian and I have some experience personally in things we have done	▶ 00:13
at Google, at NASA, and at Stanford.	▶ 00:18
And I want to tell you a little bit about some of those.	▶ 00:21
One of the best successes of AI technology at Google	▶ 00:25
has been the machine translation system.	▶ 00:28
Here we see an example of an article in Italian automatically translated into English.	▶ 00:31
Now, these systems are built for 50 different languages,	▶ 00:37
and we can translate from any of the languages into any of the other languages.	▶ 00:41
So, that's over 2,500 different systems, and we've done this all	▶ 00:46
using machine learning techniques, using AI techniques,	▶ 00:51
rather than trying to build them by hand.	▶ 00:55
And the way it works is that we go out and collect examples of text	▶ 00:58
that's a line between the 2 languages.	▶ 01:03
So we find, say, a newspaper that publishes 2 editions,	▶ 01:06
an Italian edition and an English edition, and now we have examples of translations.	▶ 01:11
And if anybody ever asked us for exactly the translation of this one particular article,	▶ 01:16
then we could just look it up and say "We already know that."	▶ 01:22
But of course, we aren't often going to be asked that.	▶ 01:25
Rather, we're going to be asked parts of this.	▶ 01:27
Here are some words that we've seen before, and we have to figure out	▶ 01:30
which words in this article correspond to which words in the translation article.	▶ 01:34
And when we do that by examining many, many millions of words of text	▶ 01:40
in the 2 languages and making the correspondence,	▶ 01:45
and then we can put that all together.	▶ 01:49
And then when we see a new example of text that we haven't seen before,	▶ 01:51
we can just look up what we've seen in the past for that correspondence.	▶ 01:54
So, the task is really two parts.	▶ 01:58
Off-line, before we see an example of text we want to translate,	▶ 02:01
we first build our translation model.	▶ 02:05
We do that by examining all of the different examples	▶ 02:07
and figuring out which part aligns to which.	▶ 02:10
Now, when we're given a text to translate, we use that model,	▶ 02:14
and we go through and find the most probable translation.	▶ 02:18
So, what does it look like?	▶ 02:22
Well, let's look at it in some example text.	▶ 02:24
And rather than look at news articles, I'm going to look at something simpler.	▶ 02:26
I'm going to switch from Italian to Chinese.	▶ 02:29
Here's a bilingual text.	▶ 02:35
Now, for a large-scale machine translation, examples are found on the Web.	▶ 02:37
This example was found in a Chinese restaurant by Adam Lopez.	▶ 02:41
Now, it's given, for a text of this form,	▶ 02:46
that a line in Chinese corresponds to a line in English,	▶ 02:49
and that's true for each of the individual lines.	▶ 02:55
But to learn from this text, what we really want to discover	▶ 02:59
is what individual words in Chinese correspond to individual words	▶ 03:02
or small phrases in English.	▶ 03:07
I've started that process by highlighting the word "wonton" in English.	▶ 03:09
It appears 3 times throughout the text.	▶ 03:16
Now, in each of those lines, there's a character that appears,	▶ 03:18
and that's the only place in the Chinese text where that character appears.	▶ 03:23
So, that seems like it's a high probability that this character in Chinese	▶ 03:27
corresponds to the word "wonton" in English.	▶ 03:33
Let's see if we can go farther.	▶ 03:36
My question for you is what word or what character or characters in Chinese	▶ 03:38
correspond to the word "chicken" in English?	▶ 03:44
And here we see "chicken" appears in these locations.	▶ 03:47
Click on the character or characters in Chinese that corresponds to "chicken."	▶ 03:54

(00:44) 12 Chinese Translation Answer

(00:29) 13 Chinese Translation Answer 2

(00:48) 14 Chinese Translation Answer 3

(00:56) 15 Congratulations

(42) Unit 2

(01:34) Topic 1, Introduction

(04:29) Topic 2, Route Finding Question

And the answer is no.	▶ 00:00
There is no solution that the agent can come up with	▶ 00:03
because Bucharest doesn't appear on the map,	▶ 00:06
and so the agent doesn't know any actions that can arrive there.	▶ 00:08
So let's give the agent a better chance.	▶ 00:12
Now we've given the agent the full map of Romania.	▶ 00:19
To start, he's in Arad, and the destination--or goal--is in Bucharest.	▶ 00:23
And the agent is given the problem of coming up with a sequence of actions	▶ 00:30
that will arrive at the destination.	▶ 00:35
Now, is it possible for the agent to solve this problem?	▶ 00:37
And the answer is yes.	▶ 00:43
There are many routes or steps or sequences of actions that will arrive at the destination.	▶ 00:45
Here is one of them:	▶ 00:50
Starting out in Arad, taking this step first, then this one, then this one,	▶ 00:53
then this one, and then this one to arrive at the destination.	▶ 01:00
So that would count as a solution to the problem.	▶ 01:05
So sequence of actions, chained together, that are guaranteed to get us to the goal.	▶ 01:08
[DEFINITION OF A PROBLEM]	▶ 01:12
Now let's formally define what a problem looks like.	▶ 01:14
A problem can be broken down into a number of components.	▶ 01:17
First, the initial state that the agent starts out with.	▶ 01:21
In our route finding problem, the initial state was the agent being in the city of Arad.	▶ 01:25
Next, a function--Actions--that takes a state as input and returns	▶ 01:32
a set of possible actions that the agent can execute when the agent is in this state.	▶ 01:41
[ACTIONS (s) {a,a2,a3...}]	▶ 01:47
In some problems, the agent will have the same actions available in all states	▶ 01:50
and in other problems, he'll have different actions dependent on the state.	▶ 01:54
In the route finding problem, the actions are dependent on the state.	▶ 01:58
When we're in one city, we can take the routes to the neighboring cities--	▶ 02:02
but we can't go to any other cities.	▶ 02:06
Next we have a function called Result, which takes, as input, a state and an action	▶ 02:09
and delivers, as its output, a new state.	▶ 02:20
So, for example, if the agent is in the city of Arad, and takes--that would be the state--	▶ 02:24
and takes the action of driving along Route E-671 towards Timisoara,	▶ 02:33
then the result of applying that action in that state would be the new state--	▶ 02:40
where the agent is in the city of Timisoara.	▶ 02:45
Next, we need a function called Goal Test,	▶ 02:51
which takes a state and returns a Boolean value--	▶ 02:58
true or false--telling us if this state is a goal or not.	▶ 03:04
In a route-finding problem, the only goal would be being in the destination city--	▶ 03:09
the city of Bucharest--and all the other states would return false for the Goal Test.	▶ 03:14
And finally, we need one more thing which is a Path Cost function--	▶ 03:19
which takes a path, a sequence of state/action transitions,	▶ 03:28
and returns a number, which is the cost of that path.	▶ 03:40
Now, for most of the problems we'll deal with, we'll make the Path Cost function be additive	▶ 03:44
so that the cost of the path is just the sum of the costs of the individual steps.	▶ 03:50
And so we'll implement this Path Cost function, in terms of a Step Cost function.	▶ 03:56
The Step Cost function takes a state, an action, and the resulting state from that action	▶ 04:04
and returns a number--n--which is the cost of that action.	▶ 04:14
In the route finding example, the cost might be the number of miles traveled	▶ 04:18
or maybe the number of minutes it takes to get to that destination.	▶ 04:24

(02:55) Topic 3, Route Finding

Now let’s see how the definition of a problem	▶ 00:00
maps onto the route finding, the domain.	▶ 00:06
First, the initial state was given.	▶ 00:10
Let’s say we start off in Arad,	▶ 00:12
and the goal test,	▶ 00:15
let’s say that the state of being in Bucharest	▶ 00:17
is the only state that counts as a goal,	▶ 00:22
and all the other states are not goals.	▶ 00:24
Now the set of all of the states here	▶ 00:26
is known as the state space,	▶ 00:29
and we navigate the state space by applying actions.	▶ 00:31
The actions are specific to each city,	▶ 00:35
so when we are in Arad, there are three possible actions,	▶ 00:39
to follow this road, this one, or this one.	▶ 00:42
And as we follow them, we build paths	▶ 00:46
or sequences of actions.	▶ 00:49
So just being in Arad is the path of length zero,	▶ 00:51
and now we could start exploring the space	▶ 00:55
and add in this path of length one,	▶ 00:58
this path of length one,	▶ 01:01
and this path of length one.	▶ 01:03
We could add in another path here of length two	▶ 01:06
and another path here of length two.	▶ 01:11
Here is another path of length two.	▶ 01:14
Here is a path of length three.	▶ 01:17
Another path of length two, and so on.	▶ 01:21
Now at ever point,	▶ 01:26
we want to separate the state out into three parts.	▶ 01:28
First, the ends of the paths—	▶ 01:34
The farthest paths that have been explored,	▶ 01:37
we call the frontier.	▶ 01:40
And so the frontier in this case	▶ 01:42
consists of these states	▶ 01:46
that are the farthest out we have explored.	▶ 01:51
And then to the left of that in this diagram,	▶ 01:55
we have the explored part of the state.	▶ 01:59
And then off to the rigtht,	▶ 02:02
we have the unexplored.	▶ 02:04
So let’s write down those three components.	▶ 02:06
We have the frontier.	▶ 02:09
We have the unexplored region,	▶ 02:15
and we have the explored region.	▶ 02:20
One more thing,	▶ 02:25
in this diagram we have labeled the step cost	▶ 02:27
of each action along the route.	▶ 02:30
So the step cost of going between Neamt to Iasi	▶ 02:33
would be 87 corresponding to a distance of 87 kilometers,	▶ 02:37
and the path cost is just the sum of the step costs.	▶ 02:42
So the cost of the path	▶ 02:46
of going from Arad to Oradea	▶ 02:48
would be 71 plus 75.	▶ 02:50

(03:19) Topic 4, Tree Search

[Narrator] Now let's define a function for solving problems.	▶ 00:00
It's called Tree Search because it superimposes	▶ 00:04
a search tree over the state space.	▶ 00:07
Here's how it works: It starts off by	▶ 00:10
initializing the frontier to be the path	▶ 00:12
consisting of only the initial states,	▶ 00:14
and then it goes into a loop	▶ 00:16
in which it first checks to see	▶ 00:18
do we still have anything left in the frontier?	▶ 00:21
If not we fail, there can be no solution.	▶ 00:23
If we do have something, then we make a choice.	▶ 00:25
Tree Search is really a family of functions	▶ 00:28
not a single algorithm which	▶ 00:31
depends on how we make that choice,	▶ 00:33
and we'll see some of the options later.	▶ 00:35
If we go ahead and make a choice of one of	▶ 00:38
the paths on the frontier and remove that	▶ 00:41
path from the frontier, we find the state	▶ 00:43
which is at the end of the path, and if that	▶ 00:45
state's a go then we're done.	▶ 00:47
We found a path to the goal; otherwise,	▶ 00:49
we do what's called expanding that path.	▶ 00:51
We look at all the actions from that state,	▶ 00:54
and we add to the path the actions	▶ 00:57
and the result of that state; so we get	▶ 01:00
a new path that has the old path, the action	▶ 01:03
and the result of that action, and we	▶ 01:06
stick all of those paths back onto the frontier.	▶ 01:09
Now Tree Search represents a whole family	▶ 01:17
of algorithms, and where you get the family	▶ 01:19
resemblance is that they're all looking	▶ 01:22
at the frontier, copying items off and	▶ 01:24
and looking to see if their goal tests,	▶ 01:26
but where you get the difference is right here,	▶ 01:29
in the choice of how you're going to expand	▶ 01:31
the next item on the frontier, which	▶ 01:34
path do we look at first, and we'll go through	▶ 01:36
different sets of algorithms that make	▶ 01:39
different choices for which path to look at first.	▶ 01:42
The first algorithm I want to consider	▶ 01:47
is called Breadth-First Search.	▶ 01:49
Now it could be called shortest-first search	▶ 01:51
because what it does is always choose	▶ 01:54
of the frontier one of the paths that hadn't been	▶ 01:56
considered yet that's the shortest possible.	▶ 01:59
So how does it work?	▶ 02:02
Well we start off with the path of	▶ 02:04
length 0, starting in the start state, and	▶ 02:06
that's the only path in the frontier so	▶ 02:10
it's the shortest one so we pick it,	▶ 02:13
and then we expand it, and we add in	▶ 02:15
all the paths that result from	▶ 02:17
applying all the possible actions.	▶ 02:20
So now we've removed	▶ 02:22
this path from the frontier,	▶ 02:25
but we've added in 3 new paths.	▶ 02:28
This one,	▶ 02:31
this one, and this one.	▶ 02:33
Now we're in a position where	▶ 02:37
we have 3 paths on the frontier, and	▶ 02:39
we have to pick the shortest one.	▶ 02:42
Now in this case all 3 paths	▶ 02:45
have the same length, length 1, so we	▶ 02:47
break the tie at random or using some	▶ 02:50
other technique, and let's suppose that	▶ 02:52
in this case we choose this path	▶ 02:56
from Arad to Sibiu.	▶ 02:58
Now the question I want you to answer	▶ 03:00
is once we remove that from the frontier,	▶ 03:03
what paths are we going to add next?	▶ 03:09
So show me by checking off the cities	▶ 03:11
that ends the paths, which paths	▶ 03:14
are going to be added to the frontier?	▶ 03:16

(02:54) Topic 5, Tree Search Answer

[Male narrator] The answer is that in Sibiu, the action function gives us 4 actions	▶ 00:00
corresponding to traveling along these 4 roads,	▶ 00:06
so we have to add in paths for each of those actions.	▶ 00:09
One of those paths goes here,	▶ 00:15
the other path continues from Arad and goes out here.	▶ 00:17
The third path continues out here	▶ 00:21
and then the fourth path goes from here--from Arad to Sibiu	▶ 00:25
and then backtracks back to Arad.	▶ 00:31
Now, it may seem silly and redundant to have a path that starts in Arad,	▶ 00:36
goes to Sibiu and returns to Arad.	▶ 00:41
How can that help us get to our destination in Bucharest?	▶ 00:44
But we can see if we're dealing with a tree search,	▶ 00:49
why it's natural to have this type of formulation	▶ 00:52
and why the tree search doesn't even notice that it's backtracked.	▶ 00:56
What the tree search does is superimpose on top of the state space	▶ 01:00
a tree of searches, and the tree looks like this.	▶ 01:05
We start off in state A, and in state A, there were 3 actions,	▶ 01:09
so we gave those paths going to Z, S, and T.	▶ 01:15
And from S, there were 4 actions, so that gave us paths going from O, F, R, and A,	▶ 01:21
and then the tree would continue on from here.	▶ 01:34
We'd take one of the next items	▶ 01:37
and we'd move it and continue on, but notice that we returned to the A state	▶ 01:40
in the state space, but in the tree,	▶ 01:48
it's just another item in the tree.	▶ 01:51
Now, here's another representation of the search space	▶ 01:55
and what's happening is as we start to explore the state,	▶ 01:57
we keep track of the frontier, which is the set of states that are at the end of the paths	▶ 02:01
that we haven't explored yet, and behind that frontier	▶ 02:09
is the set of explored states, and ahead of the frontier is the unexplored states.	▶ 02:13
Now the reason we keep track of the explored states	▶ 02:19
is that when we want to expand and we find a duplicate--	▶ 02:22
so say when we expand from here, if we pointed back to state T,	▶ 02:27
if we hadn't kept track of that, we would have to add in a new state for T down here.	▶ 02:33
But because we've already seen it and we know that this is actually a regressive step	▶ 02:42
into the already explored state, now, because we kept track of that,	▶ 02:47
we don't need it anymore.	▶ 02:51

(01:05) Topic 6, Graph Search

(00:42) Topic 7, Graph Search Answer

(00:13) Topic 8, Graph Search Answer

(00:38) Topic 9, More Graph Search

(00:29) Topic 10, Graph Search Answer

(01:30) Topic 11, Graph Search Termination

(00:51) Topic 12, Uniform Cost Search

(00:44) Topic 13, Uniform Cost Search

(00:56) Topic 14, Uniform Cost Search

(00:15) Topic 15, Uniform Cost Search

(01:13) Topic 16, Uniform Cost Termination

(01:46) Topic 17, Uniform Cost Termination Answer

(01:50) Topic 18, Depth First Search

(01:49) Topic 19, Search Optimality Answer

(02:02) Topic 20, Storage Requirements, Completeness

(00:49) Topic 21, Completeness Answer

(04:28) Topic 22, More on Uniform Cost Search

Let's try to understand a little better how uniform cost search works.	▶ 00:00
We start at a start state,	▶ 00:05
and then we start expanding out from there looking at different paths,	▶ 00:08
and what we end of doing is expanding in terms of contours like on a topological map,	▶ 00:13
where first we span out to a certain distance, then to a farther distance,	▶ 00:21
and then to a farther distance.	▶ 00:28
Now at some point we meet up with a goal. Let's say the goal is here.	▶ 00:31
Now we found a path from the start to the goal.	▶ 00:35
But notice that the search really wasn't directed at any way towards the goal.	▶ 00:42
It was expanding out everywhere in the space and depending on where the goal is,	▶ 00:46
we should expect to have to explore half the space, on average, before we find the goal.	▶ 00:52
If the space is small, that can be fine,	▶ 00:57
but when spaces are large, that won't get us to the goal fast enough.	▶ 01:00
Unfortunately, there is really nothing we can do, with what we know, to do better than that,	▶ 01:05
and so if we want to improve, if we want to be able to find the goal faster,	▶ 01:10
we're going to have to add more knowledge.	▶ 01:15
The type of knowledge that is proven most useful in search is an estimate of the distance	▶ 01:21
from the start state to the goal.	▶ 01:27
So let's say we're dealing with a route-finding problem,	▶ 01:32
and we can move in any direction--up or down, right or left--	▶ 01:36
and we'll take as our estimate, the straight line distance between a state and a goal,	▶ 01:43
and we'll try to use that estimate to find our way to the goal fastest.	▶ 01:50
Now an algorithm called greedy best-first search does exactly that.	▶ 01:55
It expands first the path that's closest to the goal according to the estimate.	▶ 02:04
So what do the contours look like in this approach?	▶ 02:09
Well, we start here, and then we look at all the neighboring states,	▶ 02:13
and the ones that appear to be closest to the goal we would expand first.	▶ 02:17
So we'd start expanding like this and like this and like this and like this	▶ 02:21
and that would lead us directly to the goal.	▶ 02:30
So now instead of exploring whole circles that go out everywhere with a certain space,	▶ 02:33
our search is directed towards the goal.	▶ 02:38
In this case it gets us immediately towards the goal, but that won't always be the case	▶ 02:41
if there are obstacles along the way.	▶ 02:46
Consider this search space. We have a start state and a goal,	▶ 02:50
and there's an impassable barrier.	▶ 02:54
Now greedy best-first search will start expanding out as before,	▶ 02:57
trying to get towards the goal,	▶ 03:02
and when it reaches the barrier, what will it do next?	▶ 03:08
Well, it will try to increase along a path that's getting closer and closer to the goal.	▶ 03:11
So it won't consider going back this way which is farther from the goal.	▶ 03:15
Rather it will continue expanding out along these lines	▶ 03:20
which always get closer and closer to the goal,	▶ 03:24
and eventually it will find its way towards the goal.	▶ 03:28
So it does find a path, and it does it by expanding a small number of nodes,	▶ 03:31
but it's willing to accept a path which is longer than other paths.	▶ 03:36
Now if we explored in the other direction, we could have found a much simpler path,	▶ 03:42
a much shorter path, by just popping over the barrier, and then going directly to the goal.	▶ 03:47
but greedy best-first search wouldn't have done that because	▶ 03:54
that would have involved getting to this point, which is this distance to the goal,	▶ 03:56
and then considering states which were farther from the goal.	▶ 04:01
What we would really like is an algorithm that combines the best parts	▶ 04:08
of greedy search which explores a small number of nodes in many cases	▶ 04:11
and uniform cost search which is guaranteed to find a shortest path.	▶ 04:17
We'll show how to do that next using an algorithm called the A-star algorithm.	▶ 04:22

(03:14) Topic 23, A-Star Search

[Male narrator] A* Search works by always expanding the path	▶ 00:00
that has a minimum value of the function f	▶ 00:03
which is defined as a sum of the g + h components.	▶ 00:07
Now, the function g of a path	▶ 00:12
is just the path cost,	▶ 00:16
and the function h of a path	▶ 00:19
is equal to the h value of the state,	▶ 00:23
which is the final state of the path,	▶ 00:27
which is equal to the estimated distance to the goal.	▶ 00:30
Here's an example of how A* works.	▶ 00:36
Suppose we found this path through the state's base to a state x	▶ 00:39
and we're trying to give a measure to the value of this path.	▶ 00:44
The measure f is a sum of g, the path cost so far,	▶ 00:48
and h, which is the estimated distance that the path will take	▶ 00:55
to complete its path to the goal.	▶ 01:02
Now, minimizing g helps us keep the path short	▶ 01:04
and minimizing h helps us keep focused on finding the goal	▶ 01:08
and the result is a search strategy that is the best possible	▶ 01:13
in the sense that it finds the shortest length path	▶ 01:17
while expanding the minimum number of paths possible.	▶ 01:20
It could be called "best estimated total path cost first,"	▶ 01:24
but the name A* is traditional.	▶ 01:28
Now let's go back to Romania and apply the A* algorithm	▶ 01:32
and we're going to use a heuristic, which is a straight line distance	▶ 01:36
between a state and the goal.	▶ 01:40
The goal, again, is Bucharest,	▶ 01:42
and so the distance from Bucharest to Bucharest is, of course, 0.	▶ 01:44
And for all the other states, I've written in red	▶ 01:47
the straight line distance.	▶ 01:51
For example, straight across like that.	▶ 01:53
Now, I should say that all the roads here I've drawn as straight lines,	▶ 01:55
but actually, roads are going to be curved to some degree,	▶ 01:59
so the actual distance along the roads is going to be longer	▶ 02:03
than the straight line distance.	▶ 02:06
Now, we start out as usual--we'll start in Arad as a start state--	▶ 02:09
and we'll expand out Arad and so we'll add 3 paths	▶ 02:13
and the evaluation function, f, will be the sum of the path length,	▶ 02:21
which is given in black, and the estimated distance,	▶ 02:26
which is given in red.	▶ 02:29
And so the path length from this path	▶ 02:32
will be 140+253 or 393;	▶ 02:37
for this path, 75+374, or 449;	▶ 02:45
and for this path, 118+329, or 447.	▶ 02:55
And now, the question is out of all the paths that are on the frontier,	▶ 03:05
which path would we expand next under the A* algorithm?	▶ 03:09

(00:14) Topic 23, A-Star Search ANSWER
The answer is that we select this path first--the one from Arad to Sibiu-- ▶ 00:00
because it has the smallest value--393--of the sum f=g+h. ▶ 00:05

(00:39) Topic 24, A-Star Second Question

(00:12) Topic 24, A-Star Second Question ANSWER

(00:20) Topic 25, A-Star Third Question

(00:11) Topic 25, A-Star Third Question ANSWER
And the answer is that we expand this path, Fagaras, next, ▶ 00:00
because its f-total, 415, ▶ 00:05
is less than all the other paths in the front tier. ▶ 00:08

(00:26) Topic 26, A-Star Fourth Question

(00:23) Topic 26, A-Star Fourth Question ANSWER

(01:24) Topic 27, A-Star Fifth Question

(00:49) Topic 27, A-Star Fifth Question ANSWER Mandatory

(01:22) Topic 28, Optimistic Heuristics

(00:59) Topic 29, State Spaces

(00:35) Topic 29, State Spaces ANSWER

(01:44) Topic 30, State Space Diagram and More Complexity

(00:57) Topic 30, State Space Diagram and More Complexity ANSWER

(01:49) Topic 31, Sliding Blocks Puzzle

(00:42) Topic 31, Sliding Blocks Puzzle ANSWER

(03:16) Topic 32, Where is the Intelligence

Now, we're trying to build an artificial intelligence	▶ 00:01
that can solve problems like this all on its own.	▶ 00:04
You can see that the search algorithms do a great job	▶ 00:08
of finding solutions to problems like this.	▶ 00:12
But, you might complain that in order for the search algorithms to work,	▶ 00:15
we had to provide it with a heurstic function.	▶ 00:19
A heurstic function came from the outside.	▶ 00:22
You might think that coming up with a good heurstic function is really where all the intelligence is.	▶ 00:25
So, a problem solver that uses an heurstic function given to it	▶ 00:30
really isn't intelligent at all.	▶ 00:34
So let's think about where the intelligence could come from	▶ 00:36
and can we automatically come up with good heurstic functions.	▶ 00:39
I'm going to sketch a description of	▶ 00:45
a program that can automatically come up with good heurstics	▶ 00:47
given a description of a problem.	▶ 00:50
Suppose this program is given a description of the sliding blocks puzzle	▶ 00:52
where we say that a block can move from square A to square B	▶ 00:57
if A is adjacent to B and B is blank.	▶ 01:02
Now, imagine that we try to loosen this restriction.	▶ 01:06
We cross out "B is blank,"	▶ 01:10
and then we get the rule	▶ 01:14
"a block can move from A to B if A is adjacent to B,"	▶ 01:16
and that's equal to our heurstic h2	▶ 01:20
because a block can move anywhere to an adjacent state.	▶ 01:23
Now, we could also cross out the other part of the rule,	▶ 01:27
and we now get "a block can move from any square A	▶ 01:31
to any square B regardless of any condition.	▶ 01:36
That gives us heurstic h1.	▶ 01:40
So we see that both of our heurstics can be derived	▶ 01:43
from a simple mechanical manipulation	▶ 01:48
of the formal description of the problem.	▶ 01:50
Once we've generated automatically these candidate heuristics,	▶ 01:53
another way to come up with a good heurstic is to say	▶ 01:58
that a new heurstic, h,	▶ 02:02
is equal to the maximum of h1 and h2,	▶ 02:04
and that's guaranteed to be admissible as long as	▶ 02:10
h1 and h2 are admissible	▶ 02:13
because it still never overestimates,	▶ 02:16
and it's guaranteed to be better because its getting closer to the true value.	▶ 02:18
The only problem with combining multiple heuristics like this	▶ 02:22
is that there is some cause to compute the heuristic	▶ 02:27
and it could take longer to compute	▶ 02:29
even if we end up expanding pure paths.	▶ 02:31
Crossing out parts of the rules like this	▶ 02:35
is called "generating a relaxed problem."	▶ 02:38
What we've done is we've taken the original problem,	▶ 02:41
where it's hard to move squares around,	▶ 02:44
and made it easier by relaxing one of the constraints.	▶ 02:46
You can see that as adding new links in the state space,	▶ 02:49
so if we have a state space in which there are only particular links,	▶ 02:54
by relaxing the problem it's as if we are adding new operators	▶ 02:59
that traverse the state in new ways.	▶ 03:05
So adding new operators only makes the problem easier,	▶ 03:07
and thus never overestimates, and thus is admissible.	▶ 03:11

(01:52) Topic 33, What Can't Search Do

(02:35) Topic 34, Note on Implementation

Our description of the algorithm has talked about paths in the state space.	▶ 00:01
I want to say a little bit now about how to implement that in terms of a computer algorithm.	▶ 00:08
We talk about paths, but we want to implement that in some ways.	▶ 00:15
In the implementation we talk about nodes.	▶ 00:19
A node is a data structure, and it has four fields.	▶ 00:22
The state field indicates the state at the end of the path.	▶ 00:27
The action was the action it took to get there.	▶ 00:35
The cost is the total cost,	▶ 00:40
and the parent is a pointer to another node.	▶ 00:45
In this case, the node that has state "S",	▶ 00:50
and it will have a parent which points to the node that has state "A",	▶ 00:56
and that will have a parent pointer that's null.	▶ 01:06
So we have a linked list of nodes representing the path.	▶ 01:10
We'll use the word "path" for the abstract idea,	▶ 01:15
and the word "node" for the representation in the computer memory.	▶ 01:18
But otherwise, you can think of those two terms as being synonyms,	▶ 01:22
because they're in a one-to-one correspondence.	▶ 01:26
Now there are two main data structures that deal with nodes.	▶ 01:31
We have the "frontier" and we have the "explored" list.	▶ 01:35
Let's talk about how to implement them.	▶ 01:41
In the frontier the operations we have to deal with	▶ 01:44
are removing the best item from the frontier and adding in new ones.	▶ 01:48
And that suggests we should implement it as a priority queue,	▶ 01:52
which knows how to keep track of the best items in proper order.	▶ 01:55
But we also need to have an additional operation	▶ 01:59
of a membership test as a new item in the frontier.	▶ 02:03
And that suggests representing it as a set,	▶ 02:07
which can be built from a hash table or a tree.	▶ 02:10
So the most efficient implementations of search actually have both representations.	▶ 02:14
The explored set, on the other hand, is easier.	▶ 02:20
All we have to do there is be able to add new members and check for membership.	▶ 02:23
So we represent that as a single set,	▶ 02:28
which again can be done with either a hash table or tree.	▶ 02:31

(16) Homework 1

(00:05) Congratulations!
Congratulations. ▶ 00:00
You just made assignment 1. ▶ 00:02
(00:05) Introduction
This is homework assignment #1. ▶ 00:00

(01:00) Question 1, Peg Solitaire

(00:22) Question 1, Peg Solitaire ANSWER

(00:54) Question 2, Loaded Coin

(00:38) Question 2, Loaded Coin ANSWER

(00:32) Question 3, Path Through Maze

(00:18) Question 3, Path Through Maze ANSWER

(00:43) Question 4, Search Tree

(00:38) Question 4, Search Tree ANSWER

(00:31) Question 5, Another Search Tree

(00:48) Question 5, Another Search Tree ANSWER

(01:00) Question 6, Search Network

(00:49) Question 6, Search Network ANSWER

(01:16) Question 7, A* Search

(01:44) Question 7, A* Search ANSWER

(64) Unit 3

(06:26) 1 Introduction

So the next units will be concerned with probabilities	▶ 00:00
and particularly with structured probabilities using Bayes networks.	▶ 00:03
This is some of the most involved material in this class.	▶ 00:08
And since this is a Stanford level class,	▶ 00:12
you will find out that some of the quizzes are actually really hard.	▶ 00:14
So as you go through the material, I hope the hardness of the quizzes won't discourage you;	▶ 00:18
it'll really entice you to take a piece of paper and a pen and work them out.	▶ 00:23
Let me give you a flavor of a Bayes network using an example.	▶ 00:30
Suppose you find in the morning that your car won't start.	▶ 00:35
Well, there's many causes why your car might not start.	▶ 00:39
One is that your battery is flat.	▶ 00:43
Even for a flat battery there is multiple causes.	▶ 00:46
One, it's just plain dead,	▶ 00:50
and one is that the battery is okay but it's not charging.	▶ 00:52
The reason why a battery might not charge is that the alternator might be broken	▶ 00:55
or the fan belt might be broken.	▶ 01:01
If you look at this influence diagram, also called a Bayes network,	▶ 01:03
you'll find there's many different ways to explain that the car won't start.	▶ 01:07
And a natural question you might have is, "Can we diagnose the problem?"	▶ 01:12
One diagnostic tool is a battery meter,	▶ 01:17
which may increase or decrease your belief that the battery may cause your car failure.	▶ 01:20
You might also know your battery age.	▶ 01:26
Older batteries tend to go dead more often.	▶ 01:29
And there's many other ways to look at reasons why the car might not start.	▶ 01:31
You might inspect the lights, the oil light, the gas gauge.	▶ 01:37
You might even dip into the engine to see what the oil level is with a dipstick.	▶ 01:43
All of those relate to alternative reasons why the car might not be starting,	▶ 01:48
like no oil, no gas, the fuel line might be blocked, or the starter may be broken.	▶ 01:52
And all of these can influence your measurements,	▶ 01:59
like the oil light or the gas gauge, in different ways.	▶ 02:04
For example, the battery flat would have an effect on the lights.	▶ 02:07
It might have an effect on the oil light and on the gas gauge,	▶ 02:12
but it won't really affect the oil you measure with the dipstick.	▶ 02:16
That is affected by the actual oil level, which also affects the oil light.	▶ 02:20
Gas will affect the gas gauge, and of course without gas the car doesn't start.	▶ 02:26
So this is a complicated structure that really describes one way to understand	▶ 02:32
how a car doesn't start.	▶ 02:39
A car is a complex system.	▶ 02:41
It has lots of variables you can't really measure immediately,	▶ 02:43
and it has sensors which allow you to understand a little bit about the state of the car.	▶ 02:46
What the Bayes network does,	▶ 02:52
it really assists you in reasoning from observable variables, like the car won't start	▶ 02:54
and the value of the dipstick, to hidden causes, like is the fan belt broken	▶ 03:01
or is the battery dead.	▶ 03:06
What you have here is a Bayes network.	▶ 03:09
A Bayes network is composed of nodes.	▶ 03:13
These nodes correspond to events that you might or might not know	▶ 03:15
that are typically called random variables.	▶ 03:21
These nodes are linked by arcs, and the arcs suggest that a child of an arc	▶ 03:24
is influenced by its parent but not in a deterministic way.	▶ 03:31
It might be influenced in a probabilistic way, which means an older battery, for example,	▶ 03:35
has a higher chance of causing the battery to be dead,	▶ 03:41
but it's not clear that every old battery is dead.	▶ 03:45
There is a total of 16 variables in this Bayes network.	▶ 03:48
What the graph structure and associated probabilities specify	▶ 03:53
is a huge probability distribution in the space of all of these 16 variables.	▶ 03:59
If they are all binary, which we'll assume throughout this unit,	▶ 04:06
they can take 2 to the 16th different values, which is a lot.	▶ 04:10
The Bayes network, as we find out, is a complex representation	▶ 04:15
of a distribution over this very, very large joint probability distribution of all of these variables.	▶ 04:18
Further, once we specify the Bayes network,	▶ 04:26
we can observe, for example, the car won't start.	▶ 04:29
We can observe things like the oil light and the lights and the battery meter	▶ 04:33
and then compute probabilities of the hypothesis, like the alternator is broken	▶ 04:37
or the fan belt is broken or the battery is dead.	▶ 04:41
So in this class we're going to talk about how to construct this Bayes network,	▶ 04:45
what the semantics are, and how to reason in this Bayes network	▶ 04:50
to find out about variables we can't observe, like whether the fan belt is broken or not.	▶ 04:56
That's an overview.	▶ 05:02
Throughout this unit I am going to assume that every event is discrete--	▶ 05:04
in fact, it's binary.	▶ 05:08
We'll start with some consideration of basic probability,	▶ 05:10
we'll work our way into some simple Bayes networks,	▶ 05:14
we'll talk about concepts like conditional independence	▶ 05:19
and then define Bayes networks more generally,	▶ 05:23
move into concepts like D-separation and start doing parameter counts.	▶ 05:26
Later on, Peter will tell you about inference in Bayes networks.	▶ 05:32
So we won't do this in this class.	▶ 05:36
I can't overemphasize how important this class is.	▶ 05:38
Bayes networks are used extensively in almost all fields of smart computer system,	▶ 05:43
in diagnostics, for prediction, for machine learning, and fields like finance,	▶ 05:49
inside Google, in robotics.	▶ 05:57
Bayes networks are also the building blocks of more advanced AI techniques	▶ 06:00
such as particle filters, hidden Markov models, MDPs and POMDPs,	▶ 06:05
Kalman filters, and many others.	▶ 06:12
These are words that don't sound familiar quite yet,	▶ 06:14
but as you go through the class, I can promise you you will get to know what they mean.	▶ 06:18
So let's start now at the very, very basics.	▶ 06:22

(00:40) 2 Probabilities

(00:19) 2a Answer

(00:08) 2b Question
[Thrun] Let me ask my next quiz. ▶ 00:00
Suppose the probability of heads is a quarter, 0.25. ▶ 00:02
What's the probability of tail? ▶ 00:06

(00:17) 2c Answer

(00:14) 2d Question

(00:14) 2e Answer

(00:32) 2f Question

(00:23) 2g Answer

(00:10) 2h Question
[Thrun] So here's another one. ▶ 00:00
What's the probability that within the set of X1, X2, X3, and X4 ▶ 00:02
there are at least three heads? ▶ 00:07

(00:28) 2i Answer

(00:45) 2j Summary

(01:04) 3 Dependence

(01:06) 3a Answer

(01:07) 4 What We Learned

(00:25) 5 Weather Quiz

(00:05) 5a Answer
Well, the correct answer is 0.2, which is a negation of this event over here. ▶ 00:00
(00:13) 5b Question
A sunny day follows a rainy day with 0.6 chance, ▶ 00:00
and a rainy day follows a rainy day-- ▶ 00:06
please give me your number. ▶ 00:11
(00:03) 5c Answer
0.4 ▶ 00:00

(00:18) 5d Question

(01:25) 5e Answer

(00:19) 6 Cancer Quiz

(00:28) 6a Answer and Cancer Test

(01:01) 6b Answer

(00:40) 6c Answer

(00:07) 6d Question
Now, our next quiz, I want you to fill in the probability of ▶ 00:00
the cancer given that we just received a positive test. ▶ 00:04

(01:52) 6e Answer

(03:34) 7 Bayes Rule

So, we've just learned about what's probably the most important	▶ 00:00
piece of math for this class in statistics called Bayes Rule.	▶ 00:03
It was invented by Reverend Thomas Bayes, who was a British mathematician	▶ 00:09
and a Presbyterian minister in the 18th century.	▶ 00:15
Bayes Rule is usually stated as follows: P of A given B where B is the evidence	▶ 00:18
and A is the variable we care about is P of B given A times P of A over P of B.	▶ 00:27
This expression is called the likelihood.	▶ 00:36
This is called the prior, and this is called marginal likelihood.	▶ 00:40
The expression over here is called the posterior.	▶ 00:46
The interesting thing here is the way the probabilities are reworded.	▶ 00:50
Say we have evidence B.	▶ 00:55
We know about B, but we really care about the variable A.	▶ 00:57
So, for example, B is a test result.	▶ 01:01
We don't care about the test result as much as we care about the fact	▶ 01:03
whether we have cancer or not.	▶ 01:06
This diagnostic reasoning--which is from evidence to its causes--	▶ 01:08
is turned upside down by Bayes Rule into a causal reasoning,	▶ 01:16
which is given--hypothetically, if we knew the cause,	▶ 01:22
what would be the probability of the evidence we just observed.	▶ 01:27
But to correct for this inversion, we have to multiply	▶ 01:31
by the prior of the cause to be the case in the first place,	▶ 01:36
in this case, having cancer or not,	▶ 01:40
and divide it by the probability of the evidence, P(B),	▶ 01:42
which often is expanded using the theorem of total probability as follows.	▶ 01:47
The probability of B is a sum over all probabilities of B	▶ 01:52
conditional on A, lower caps a, times the probability of A equals lower caps a.	▶ 01:58
This is total probability as we already encountered it.	▶ 02:04
So, let's apply this to the cancer case	▶ 02:08
and say we really care about whether you have cancer,	▶ 02:10
which is our cause, conditioned on the evidence	▶ 02:13
that is the result of this hidden cause, in this case, a positive test result.	▶ 02:17
Let's just plug in the numbers.	▶ 02:23
Our likelihood is the probability of seeing a positive test result	▶ 02:25
given that you have cancer multiplied by the prior probability	▶ 02:30
of having cancer over the probability of the positive test result,	▶ 02:33
and that is--according to the tables we looked at before--	▶ 02:38
0.9 times a prior of 0.01 over--	▶ 02:43
now we're going to expand this right over here according to total probability	▶ 02:50
which gives us 0.9 times 0.01.	▶ 02:55
That's the probability of + given that we do have cancer.	▶ 03:01
So, the probability of + given that we don't have cancer is 0.2,	▶ 03:06
but the prior here is 0.99.	▶ 03:11
So, if we plug in the numbers we know about, we get 0.009	▶ 03:15
over 0.009 + 0.198.	▶ 03:20
That is approximately 0.0434, which is the number we saw before.	▶ 03:27

(01:52) 7a Bayes Rule Graphically

(00:24) 7b Answer

(02:32) 8 More Complex Bayes Networks

So, we just encountered our very first Bayes network	▶ 00:00
and did a number of interesting calculations.	▶ 00:03
Let's now talk about Bayes Rule and look into more complex Bayes networks.	▶ 00:06
I will look at Bayes Rule again and make an observation	▶ 00:10
that is really non-trivial.	▶ 00:13
Here is Bayes Rule, and in practice, what we find is	▶ 00:15
this term here is relatively easy to compute.	▶ 00:20
It's just a product, whereas this term is really hard to compute.	▶ 00:23
However, this term over here does not depend on what we assume for variable A.	▶ 00:28
It's just the function of B.	▶ 00:33
So, suppose for a moment we also care about the complementary event of not A	▶ 00:35
given B, for which Bayes Rule unfolds as follows.	▶ 00:40
Then we find that the normalizer, P(B), is identical,	▶ 00:43
whether we assume A on the left side or not A on the left side.	▶ 00:47
We also know from prior work that P(A) given B plus	▶ 00:51
P of not A given B must be one because these are 2 complementary events.	▶ 00:57
That allows us to compute Bayes Rule very differently	▶ 01:03
by basically ignoring the normalizer, so here's how it goes.	▶ 01:06
We compute P(A) given B--and I want to call this prime,	▶ 01:11
because it's not a real probability--to be just P(B) given A times P(A),	▶ 01:16
which is the normalizer, so the denominator of the expression over here.	▶ 01:23
We do the same thing with not A.	▶ 01:28
So, in both cases, we compute the posterior probability non-normalized	▶ 01:31
by omitting the normalizer B.	▶ 01:36
And then we can recover the original probabilities by normalizing	▶ 01:38
based on those values over here, so the probability of A given B,	▶ 01:43
the actual probability, is a normalizer, eta,	▶ 01:48
times this non-normalized form over here.	▶ 01:52
The same is true for the negation of A over here.	▶ 01:55
And eta is just the normalizer that results by adding these 2 values over here together	▶ 01:59
as shown over here, and dividing them for one.	▶ 02:06
So, take a look at this for a moment.	▶ 02:10
What we've done is we deferred the calculation of the normalizer over here	▶ 02:13
by computing pseudo probabilities that are non-normalized.	▶ 02:18
This made the calculation much easier, and when we were done with everything,	▶ 02:22
we just folded it back into the normalizer based on the resulting	▶ 02:26
pseudo probabilities and got the correct answer.	▶ 02:29

(01:08) 8a Two Test Cancer Example

(02:00) 8b Answer

(00:10) 8c Question
Calculate for me the probability of cancer ▶ 00:00
given that I received one positive and one negative test result. ▶ 00:03
Please write your number into this box. ▶ 00:08

(01:03) 8d Answer

(02:45) 9 Conditional Independence

I want to use a few words of terminology.	▶ 00:00
This, again, is a Bayes network, of which the hidden variable C	▶ 00:03
causes the still stochastic test outcomes T1 and T2.	▶ 00:08
And what is really important is that we assume not just	▶ 00:16
that T1 and T2 are identically distributed.	▶ 00:19
We use the same 0.9 for test 1 as we use for test 2,	▶ 00:22
but we also assume that they are conditionally independent.	▶ 00:27
We assumed that if God told us whether we actually had cancer or not,	▶ 00:31
if we knew with absolute certainty the value of the variable C,	▶ 00:37
that knowing anything about T1 would not help us make a statement about T2.	▶ 00:41
Put differently, we assumed that the probability of T2 given C and T1	▶ 00:48
is the same as the probability of T2 given C.	▶ 00:55
This is called conditional independence, which is given the value of the cancer variable C.	▶ 01:00
If you knew this for a fact, then T2 would be independent of T1.	▶ 01:08
It's conditionally independent because the independence only holds true	▶ 01:17
if we actually know C, and it comes out of this diagram over here.	▶ 01:21
If we look at this diagram, if you knew the variable C over here,	▶ 01:26
then C separately causes T1 and T2.	▶ 01:32
So, as a result, if you know C, whatever counted over here	▶ 01:39
is kind of cut off causally from what happens over here.	▶ 01:46
That causes these 2 variables to be conditionally independent.	▶ 01:48
So, conditional independence is a really big thing in Bayes networks.	▶ 01:52
Here's a Bayes network where A causes B and C,	▶ 01:58
and for a Bayes network of this structure, we know that given A,	▶ 02:02
B and C are independent.	▶ 02:08
It's written as B conditionally independent of C given A.	▶ 02:11
So, here's a question.	▶ 02:16
Suppose we have conditional independence between B and C given A.	▶ 02:18
Would that imply--and there's my question--that B and C are independent?	▶ 02:21
So, suppose we don't know A.	▶ 02:28
We don't know whether we have cancer, for example.	▶ 02:30
What that means is that the test results individually are still independent of each other	▶ 02:33
even if we don't know about the cancer situation.	▶ 02:38
Please answer yes or no.	▶ 02:42

(00:41) 9a Answer

(00:35) 9b Question

(02:52) 9c Answer

So, for this one, we want to apply total probability.	▶ 00:00
This thing over here is the same as probability of test 2 to be positive,	▶ 00:04
which I'm going to abbreviate with a +2 over here,	▶ 00:10
conditioned on test 1 being positive and me having cancer	▶ 00:14
times the probability of me having cancer given test 1 was positive plus	▶ 00:19
the probability of test 2 being positive conditioned on test 1 being positive	▶ 00:25
and me not having cancer times the probability of me not having cancer	▶ 00:31
given that test 1 is positive.	▶ 00:36
That's the same as the theorem of total probability,	▶ 00:38
but now everything is conditioned on +1.	▶ 00:42
Take a moment to verify this.	▶ 00:46
Now, here I can plug in the numbers.	▶ 00:48
You already calculated this one before, which is approximately 0.043,	▶ 00:50
and this one over here is 1 minus that, which is 0.957 approximately.	▶ 00:57
And this term over here now exploits conditional independence,	▶ 01:05
which is given that I know C, knowledge of the first test	▶ 01:09
gives me no more information about the second test.	▶ 01:14
It only gives me information if C was unknown, as was the case over here.	▶ 01:17
So, I can rewrite this thing over here as follows:	▶ 01:21
P of +2 given that I have cancer.	▶ 01:24
I can drop the +1, and the same is true over here.	▶ 01:27
This is exploiting my conditional independence.	▶ 01:31
I knew that P of +1 or +2 conditioned on C	▶ 01:34
is the same as P of +2 conditioned on C and +1.	▶ 01:41
I can now read those off my table over here,	▶ 01:47
which is 0.9 times 0.043 plus 0.2,	▶ 01:50
which is 1 minus 0.8 over here times 0.957,	▶ 01:58
which gives me approximately 0.2301.	▶ 02:03
So, that says if my first test comes in positive,	▶ 02:09
I expect my second test to be positive with probably 0.2301.	▶ 02:14
That's an increased probability to the default probability,	▶ 02:21
which we calculated before, which is the probability of any test,	▶ 02:24
test 2 come in as positive before was the normalizer of Bayes rule which was 0.207.	▶ 02:29
So, my first test has a 20% chance of coming in positive.	▶ 02:38
My second test, after seeing a positive test,	▶ 02:43
has now an increased probability of about 23% of coming in positive.	▶ 02:47

(00:27) 9d Absolute vs Conditional Independence

(00:45) 9e Answer

(01:59) 10 Different Type of Bayes Network

(00:55) 10a Answer

(01:51) 11 Explaining Away

(01:32) 11a Answer

(00:31) 11b Question

(02:53) 11c Answer

(01:42) 11d Question

(01:18) 11e Answer

(03:13) 11f Conclusion

[Thrun] It's really interesting to compare this to the situation over here.	▶ 00:00
In both cases I'm happy, as shown over here,	▶ 00:04
and I ask the same question, which is whether I got a raise at work, as R over here.	▶ 00:08
But in one case I observe that the weather is sunny; in the other one it isn't.	▶ 00:15
And look what it does to my probability of having received a raise.	▶ 00:21
The sunniness perfectly well explains my happiness,	▶ 00:25
and my probability of having received a raise ends up to be a mere 1.4%, or 0.014.	▶ 00:30
However, if my wife observes it to be non-sunny, then it is much more likely	▶ 00:41
that the cause of my happiness is related to a raise at work,	▶ 00:47
and now the probability is 8.3%, which is significantly higher than the 1.4% before.	▶ 00:51
This is a Bayes network of which S and R are independent	▶ 00:58
but H adds a dependence between S and R.	▶ 01:04
Let me talk about this in a little bit more detail on the next paper.	▶ 01:10
So here is our Bayes network again.	▶ 01:16
In our previous exercises, we computed for this network	▶ 01:18
that the probability of a raise of R given any of these variables shown here was as follows.	▶ 01:22
The really interesting thing is that in the absence of information about H,	▶ 01:29
which is the middle case over here,	▶ 01:34
the probability of R is unaffected by knowledge of S--	▶ 01:37
that is, R and S are independent.	▶ 01:41
This is the same as probability of R,	▶ 01:46
and R and S are independent.	▶ 01:49
However, if I know something about the variable H,	▶ 01:56
then S and R become dependent--	▶ 02:02
that is, knowing about my happiness over here renders S and R dependent.	▶ 02:06
This is not the same as probability of just R given H.	▶ 02:15
Obviously, it isn't because if I now vary S from S to not S,	▶ 02:23
it affects my probability for the variable R.	▶ 02:28
That is a really unusual situation	▶ 02:33
where we have R and S are independent	▶ 02:36
but given the variable H, R and S are not independent anymore.	▶ 02:40
So knowledge of H makes 2 variables that previously were independent non-independent.	▶ 02:50
Offered differently, 2 variables that are independent may not be in certain cases	▶ 02:58
conditionally independent.	▶ 03:06
Independence does not imply conditional independence.	▶ 03:08

(02:53) 12 General Bayes Networks

[Thrun] So we're now ready to define Bayes networks in a more general way.	▶ 00:00
Bayes networks define probability distributions over graphs or random variables.	▶ 00:05
Here is an example graph of 5 variables,	▶ 00:10
and this Bayes network defines the distribution over those 5 random variables.	▶ 00:14
Instead of enumerating all possibilities of combinations of these 5 random variables,	▶ 00:19
the Bayes network is defined by probability distributions	▶ 00:24
that are inherent to each individual node.	▶ 00:28
For node A and B, we just have a distribution P of A and P of B	▶ 00:32
because A and B have no incoming arcs.	▶ 00:38
C is a conditional distribution conditioned on A and B.	▶ 00:42
D and E are conditioned on C.	▶ 00:47
The joint probability represented by a Bayes network	▶ 00:52
is the product of various Bayes network probabilities	▶ 00:56
that are defined over individual nodes	▶ 01:00
where each node's probability is only conditioned on the incoming arcs.	▶ 01:03
So A has no incoming arc; therefore, we just want it P of A.	▶ 01:08
C has 2 incoming arcs, so we define the probability of C conditioned on A and B.	▶ 01:12
And D and E have 1 incoming arc that's shown over here.	▶ 01:18
The definition of this joint distribution by using the following factors	▶ 01:22
has one really big advantage.	▶ 01:27
Whereas the joint distribution over any 5 variables requires 2 to the 5 minus 1,	▶ 01:30
which is 31 probability values,	▶ 01:40
the Bayes network over here only requires 10 such values.	▶ 01:43
P of A is one value, for which we can derive P of not A.	▶ 01:48
Same for P of B.	▶ 01:53
P of C given A B is derived by a distribution over C	▶ 01:55
conditioned on any combination of A and B, of which there are 4 of A and B as binary.	▶ 02:02
P of D given C is 2 parameters for P of D given C and P of D given not C.	▶ 02:07
And the same is true for P of E given C.	▶ 02:15
So if you add those up, you get 10 parameters in total.	▶ 02:18
So the compactness of the Bayes network	▶ 02:21
leads to a representation that scales significantly better to large networks	▶ 02:25
than the common natorial approach which goes through all combinations of variable values.	▶ 02:31
That is a key advantage of Bayes networks,	▶ 02:36
and that is the reason why Bayes networks are being used so extensively	▶ 02:39
for all kinds of problems.	▶ 02:43
So here is a quiz.	▶ 02:45
How many probability values are required to specify this Bayes network?	▶ 02:47
Please put your answer in the following box.	▶ 02:51

(00:19) 12a Answer

(00:17) 12b Question

(00:16) 12c Answer

(00:28) 12d Question

(00:24) 12e Answer

(00:20) 12f Value of the Network

(00:35) 13 D-Separation

(00:52) 13a Answer

(00:45) 13b D-Separation Example

(01:26) 13c Answer

(02:54) 13d D-Separation General Definition

[Thrun] This leads me to the general study of conditional independence in Bayes networks,	▶ 00:00
often called D-separation or reachability.	▶ 00:06
D-separation is best studied by so-called active triplets and inactive triplets	▶ 00:10
where active triplets render variables dependent	▶ 00:17
and inactive triplets render them independent.	▶ 00:20
Any chain of 3 variables like this makes the initial and final variable dependent	▶ 00:23
if all variables are unknown.	▶ 00:30
However, if the center variable is known--	▶ 00:32
that is, it's behind the conditioning bar--	▶ 00:35
then this variable and this variable become independent.	▶ 00:38
So if we have a structure like this and it's quote-unquote cut off	▶ 00:42
by a known variable in the middle, that separates or deseparates	▶ 00:47
the left variable from the right variable, and they become independent.	▶ 00:53
Similarly, any structure like this renders the left variable and the right variable dependent	▶ 00:57
unless the center variable is known,	▶ 01:04
in which case the left and right variable become independent.	▶ 01:08
Another active triplet now requires knowledge of a variable.	▶ 01:12
This is the explain away case.	▶ 01:16
If this variable is known for a Bayes network that converges into a single variable,	▶ 01:19
then this variable and this variable over here become dependent.	▶ 01:25
Contrast this with a case where all variables are unknown.	▶ 01:29
A situation like this means that this variable on the left or on the right are actually independent.	▶ 01:33
In a single final example, we also get dependence if we have the following situation:	▶ 01:40
a direct successor of a conversion variable is known.	▶ 01:48
So it is sufficient if a successor of this variable is known.	▶ 01:52
The variable itself does not have to be known,	▶ 01:57
and the reason is if you know this guy over here,	▶ 01:59
we get knowledge about this guy over here.	▶ 02:02
And by virtue of that, the case over here essentially applies.	▶ 02:05
If you look at those rules,	▶ 02:09
those rules allow you to determine for any Bayes network	▶ 02:11
whether variables are dependent or not dependent given the evidence you have.	▶ 02:15
If you color the nodes dark for which you do have evidence,	▶ 02:20
then you can use these rules to understand whether any 2 variables	▶ 02:25
are conditionally independent or not.	▶ 02:29
So let me ask you for this relatively complicated Bayes network the following questions.	▶ 02:31
Is F independent of A?	▶ 02:37
Is F independent of A given D?	▶ 02:41
Is F independent of A given G?	▶ 02:45
And is F independent of A given H?	▶ 02:49
Please mark your answers as you see fit.	▶ 02:51

(01:03) 13e Answer

(00:50) 14 Congratulations

(34) Unit 4

(04:38) 1 Probabilistic Inference

[Probabilistic Interference]	▶ 00:00
[Male] Welcome back. In the previous unit, we went over the basics	▶ 00:02
of probability theory and saw how	▶ 00:05
a Bayes network could concisely represent a joint probability distribution,	▶ 00:12
including the representation of independence between the variables.	▶ 00:17
In this unit, we will see how to do probabilistic inference.	▶ 00:24
That is, how to answer probability questions using Bayes nets.	▶ 00:31
Let's put up a simple Bayes net.	▶ 00:36
We'll use the familiar example of the earthquake	▶ 00:40
where we can have a burglary or an earthquake	▶ 00:45
setting off an alarm, and if the alarm goes off,	▶ 00:50
either John or Mary might call.	▶ 00:53
Now, what kinds of questions can we ask to do inference about?	▶ 00:58
The simplest type of question is the same question we ask	▶ 01:02
with an ordinary subroutine or function in a programming language.	▶ 01:05
Namely, given some inputs, what are the outputs?	▶ 01:08
So, in this case, we could say given the inputs of B and E,	▶ 01:12
what are the outputs, J and M?	▶ 01:18
Rather than call them input and output variables,	▶ 01:22
in probabilistic inference, we'll call them evidence and query variables.	▶ 01:26
That is, the variables that we know the values of are the evidence,	▶ 01:36
and the ones that we want to find out the values of are the query variables.	▶ 01:39
Anything that is neither evidence nor query is known as a hidden variable.	▶ 01:44
That is, we won't tell you what its value is.	▶ 01:52
We won't figure out what its value is and report it,	▶ 01:55
but we'll have to compute with it internally.	▶ 01:58
And now furthermore, in probabilistic inference,	▶ 02:01
the output is not a single number for each of the query variables,	▶ 02:05
but rather, it's a probability distribution.	▶ 02:10
So, the answer is going to be a complete, joint probability distribution	▶ 02:13
over the query variables.	▶ 02:17
We call this the posterior distribution, given the evidence,	▶ 02:19
and we can write it like this.	▶ 02:23
It's the probability distribution of one or more query variables	▶ 02:26
given the values of the evidence variables.	▶ 02:34
And there can be zero or more evidence variables,	▶ 02:39
and each of them are given an exact value.	▶ 02:42
And that's the computation we want to come up with.	▶ 02:47
There's another question we can ask.	▶ 02:53
Which is the most likely explanation?	▶ 02:56
That is, out of all the possible values for all the query variables,	▶ 02:58
which combination of values has the highest probability?	▶ 03:03
We write the formula like this, asking which Q values	▶ 03:08
are maxable given the evidence values.	▶ 03:12
Now, in an ordinary programming language, each function goes only one way.	▶ 03:16
It has input variables, does some computation,	▶ 03:22
and comes up with a result variable or result variables.	▶ 03:26
One great thing about Bayes nets is that we're not restricted	▶ 03:31
to going only in one direction.	▶ 03:34
We could go in the causal direction, giving as evidence	▶ 03:36
the route nodes of the tree and asking as query values the nodes at the bottom.	▶ 03:41
Or, we could reverse that causal flow.	▶ 03:47
For example, we could have J and M be the evidence variables	▶ 03:50
and B and E be the query variables,	▶ 03:55
or we could have any other combination.	▶ 03:58
For example, we could have M be the evidence variable	▶ 04:01
and J and B be the query variables.	▶ 04:05
Here's a question for you.	▶ 04:11
Imagine the situation where Mary has called to report that the alarm is going off,	▶ 04:13
and we want to know whether or not there has been a burglary.	▶ 04:18
For each of the nodes, click on the circle to tell us	▶ 04:22
if the node is an evidence node, a hidden node,	▶ 04:27
or a query node.	▶ 04:32

(00:11) 1a Answer
The answer is that Mary calling is the evidence node. ▶ 00:00
The burglary is the query node, ▶ 00:04
and all the others are hidden variables in this case. ▶ 00:07

(04:24) 2 Enumeration

Now we're going to talk about how to do inference on Bayes net.	▶ 00:00
We'll start with our familiar network, and we'll talk about a method	▶ 00:04
called enumeration,	▶ 00:08
which goes through all the possibilities, adds them up,	▶ 00:12
and comes up with an answer.	▶ 00:15
So, what we do is start by stating the problem.	▶ 00:17
We're going to ask the question of what is the probability	▶ 00:24
that the burglar alarm occurred given that John called and Mary called?	▶ 00:27
We'll use the definition of conditional probability to answer this.	▶ 00:34
So, this query is equal to the joint probability distribution	▶ 00:39
of all 3 variables divided by the conditionalized variables.	▶ 00:47
Now, note I'm using a notation here where instead of writing out the probability	▶ 00:55
of some variable equals true, I'm just using the notation plus	▶ 01:01
and then the variable name in lower case,	▶ 01:05
and if I wanted the negation, I would use negation sign.	▶ 01:08
Notice there's a different notation where instead of writing out	▶ 01:13
the plus and negation signs, we just use the variable name itself, P(e),	▶ 01:17
to indicate E is true.	▶ 01:22
That notation works well, but it can get confusing between	▶ 01:25
does P(e) mean E is true, or does it mean E is a variable?	▶ 01:29
And so we're going to stick to the notation where we explicitly have	▶ 01:34
the pluses and negation signs.	▶ 01:37
To do inference by enumeration, we first take a conditional probability	▶ 01:41
and rewrite it as unconditional probabilities.	▶ 01:45
Now we enumerate all the atomic probabilities and calculate the sum of products.	▶ 01:49
Let's look at just the complex term on the numerator first.	▶ 01:56
The procedure for figuring out the denominator would be similar, and we'll skip that.	▶ 02:00
So, the probability of these 3 terms together	▶ 02:05
can be determined by enumerating all possible values of the hidden variables.	▶ 02:12
In this case, there are 2, E and A,	▶ 02:17
so we'll sum over those variables for all values of E and for all values of A.	▶ 02:22
In this case, they're boolean, so there's only 2 values of each.	▶ 02:29
We ask what's the probability of this unconditional term?	▶ 02:34
And that we get by summing out over all possibilities,	▶ 02:41
E and A being true or false.	▶ 02:44
Now, to get the values of these atomic events,	▶ 02:49
we'll have to rewrite this equation in a form that corresponds	▶ 02:52
to the conditional probability tables that we have associated with the Bayes net.	▶ 02:55
So, we'll take this whole expression and rewrite it.	▶ 03:00
It's still a sum over the hidden variables E and A,	▶ 03:04
but now I'll rewrite this expression in terms of the parents	▶ 03:08
of each of the nodes in the network.	▶ 03:12
So, that gives us the product of these 5 terms,	▶ 03:15
which we then have to sum over all values of E and A.	▶ 03:21
If we call this product f(e,a),	▶ 03:24
then the whole answer is the sum of F for all values of E and A,	▶ 03:31
so as the sum of 4 terms where each of the terms is a product of 5 numbers.	▶ 03:43
Where do we get the numbers to fill in this equation?	▶ 03:51
From the conditional probability tables from our model,	▶ 03:54
so let's put the equation back up, and we'll ask you for the case	▶ 03:58
where both E and A are positive	▶ 04:03
to look up in the conditional probability tables and fill in the numbers	▶ 04:09
for each of these 5 terms, and then multiply them together and fill in the product.	▶ 04:14

(01:59) 2a Answer

(03:27) 3 Speeding up Enumeration

[Norvig] We've seen how to do enumeration to solve the inference problem	▶ 00:00
on belief networks.	▶ 00:04
For a simple network like the alarm network, that's all we need to know.	▶ 00:06
There's only 5 variables, so even if all 5 of them were hidden,	▶ 00:10
there would only be 32 rows in the table to sum up.	▶ 00:14
From a theoretical point of view, we're done.	▶ 00:20
But from a practical point of view, other networks could give us trouble.	▶ 00:22
Consider this network, which is one for determining insurance for car owners.	▶ 00:26
There are 27 different variables.	▶ 00:35
If each of the variables were boolean, that would give us over 100 million rows to sum out.	▶ 00:38
But in fact, some of the variables are non-boolean,	▶ 00:44
they have multiple values, and it turns out that representing this entire network	▶ 00:46
and doing enumeration we'd have to sum over a quadrillion rows.	▶ 00:52
That's just not practical, so we're going to have to come up with methods	▶ 00:57
that are faster than enumerating everything.	▶ 01:01
The first technique we can use to get a speed-up in doing inference on Bayes nets	▶ 01:04
is to pull out terms from the enumeration.	▶ 01:09
For example, here the probability of b is going to be the same for all values of E and a.	▶ 01:13
So we can take that term and move it out of the summation,	▶ 01:20
and now we have a little bit less work to do.	▶ 01:26
We can multiply by that term once rather than having it in each row of the table.	▶ 01:28
We can also move this term, the P of e, to the left of the summation over a,	▶ 01:33
because it doesn't depend on a.	▶ 01:40
By doing this, we're doing less work.	▶ 01:43
The inner loop of the summation now has only 3 terms rather than 5 terms.	▶ 01:45
So we've reduced the cost of doing each row of the table.	▶ 01:50
But we still have the same number of rows in the table,	▶ 01:53
so we're going to have to do better than that.	▶ 01:57
The next technique for efficient inference is to maximize independence of variables.	▶ 02:00
The structure of a Bayes net determines how efficient it is to do inference on it.	▶ 02:08
For example, a network that's a linear string of variables,	▶ 02:12
X1 through Xn, can have inference done in time proportional to the number n,	▶ 02:17
whereas a network that's a complete network	▶ 02:27
where every node points to every other node and so on could take time 2 to the n	▶ 02:31
if all n variables are boolean variables.	▶ 02:40
In the alarm network we saw previously, we took care	▶ 02:45
to make sure that we had all the independence relations represented	▶ 02:50
in the structure of the network.	▶ 02:54
But if we put the nodes together in a different order,	▶ 02:57
we would end up with a different structure.	▶ 03:00
Let's start by ordering the node John calls first	▶ 03:03
and then adding in the node Mary calls.	▶ 03:09
The question is, given just these 2 nodes and looking at the node for Mary calls,	▶ 03:13
is that node dependent or independent of the node for John calls?	▶ 03:19

(00:24) 3a Answer

(00:13) 3b Second Question

(00:33) 3c Second Answer

(00:11) 3d Third Question
[Norvig] Now we'll continue and we'll add the node B for burglary ▶ 00:01
and ask again, click on all the variables that B is dependent on. ▶ 00:05
(00:10) 3e Third Answer
[Norvig] The answer is that B is dependent only on A. ▶ 00:00
In other words, B is independent of J and M given A. ▶ 00:04
(00:07) 3f Fourth Question
[Norvig] And finally, we'll add the last node, E, ▶ 00:00
and ask you to click on all the nodes that E is dependent on. ▶ 00:04

(00:26) 3g Fourth Answer

(00:18) 3h Causal Direction

(04:40) 4 Variable Elimination

Let's return to this equation, which we use to show how to do inference by enumeration.	▶ 00:00
In this equation, we join up the whole joint distribution	▶ 00:06
before we sum out over the hidden variables.	▶ 00:10
That's slow, because we end up repeating a lot of work.	▶ 00:15
Now we're going to show a new technique called variable elimination,	▶ 00:18
which in many networks operates much faster.	▶ 00:25
It's still a difficult computation, an NP-hard computation,	▶ 00:27
to do inference over Bayes nets in general.	▶ 00:30
Variable elimination works faster than inference by enumeration	▶ 00:34
in most practical cases.	▶ 00:38
It requires an algebra for manipulating factors,	▶ 00:41
which are just names for multidimensional arrays	▶ 00:45
that come out of these probabilistic terms.	▶ 00:48
We'll use another example to show how variable elimination works.	▶ 00:53
We'll start off with a network that has 3 boolean variables.	▶ 00:57
R indicates whether or not it's raining.	▶ 01:00
T indicates whether or not there's traffic,	▶ 01:04
and T is dependent on whether it's raining.	▶ 01:12
And finally, L indicates whether or not I'll be late for my next appointment,	▶ 01:15
and that depends on whether or not there's traffic.	▶ 01:19
Now we'll put up the conditional probability tables for each of these 3 variables.	▶ 01:22
And then we can use inference to figure out the answer to questions like	▶ 01:29
am I going to be late?	▶ 01:35
And we know by definition that we could do that through enumeration	▶ 01:38
by going through all the possible values for R and T	▶ 01:42
and summing up the product of these 3 nodes.	▶ 01:47
Now, in a simple network like this, straight enumeration would work fine,	▶ 01:54
but in a more complex network, what variable elimination does is give us a way	▶ 01:59
to combine together parts of the network into smaller parts	▶ 02:03
and then enumerate over those smaller parts and then continue combining.	▶ 02:09
So, we start with a big network.	▶ 02:13
We eliminate some of the variables.	▶ 02:15
We compute by marginalizing out, and then we have a smaller network to deal with,	▶ 02:17
and we'll show you how those 2 steps work.	▶ 02:24
The first operation in variable elimination is called joining factors.	▶ 02:28
A factor, again, is one of these tables.	▶ 02:35
It's a multidimensional matrix, and what we do is choose 2 of the factors,	▶ 02:39
2 or more of the factors.	▶ 02:43
In this case, we'll choose these 2, and we'll combine them together	▶ 02:45
to form a new factor which represents	▶ 02:49
the joint probability of all the variables in that factor.	▶ 02:52
In this case, R and T.	▶ 02:56
Now we'll draw out that table.	▶ 03:00
In each case, we just look up in the corresponding table,	▶ 03:03
figure out the numbers, and multiply them together.	▶ 03:06
For example, in this row we have a +r and a +t,	▶ 03:08
so the +r is 0.1, and the entry for +r and +t is 0.8,	▶ 03:13
so multiply them together and you get 0.08.	▶ 03:19
Go all the way down. For example, in the last row we have a -r and a -t.	▶ 03:22
-r is 0.9. The entry for -r and -t is also 0.9.	▶ 03:28
Multiply those together and you get 0.81.	▶ 03:34
So, what have we done?	▶ 03:40
We used the operation of joining factors on these 2 factors,	▶ 03:42
getting us a new factor which is part of the existing network.	▶ 03:45
Now we want to apply a second operation called elimination,	▶ 03:50
also called summing out or marginalization, to take this table and reduce it.	▶ 03:56
Right now, the tables we have look like this.	▶ 04:02
We could sum out or marginalize over the variable R	▶ 04:06
to give us a table that just operates on T.	▶ 04:10
So, the question is to fill in this table for P(T)--	▶ 04:14
there will be 2 entries in this table, the +t entry, formed by summing out	▶ 04:20
all the entries here for all values of r for which t is positive,	▶ 04:23
and the -t entry, formed the same way, by looking in this table	▶ 04:28
and summing up all the rows over all values of r where t is negative.	▶ 04:32
Put your answers in these boxes.	▶ 04:37

(00:27) 4a Answer

(00:28) 4b More Variable Elimination

(00:38) 4c Answer

(00:30) 4d Even More Variable Elimination

(00:20) 4e Answer
The answer is that the +l values, ▶ 00:00
0.051 plus 0.083 equals 0.134. ▶ 00:03
And the negative values, 0.119 plus 0.747 ▶ 00:11
equals 0.886. ▶ 00:15
((??:??)) 4f Summary
No subtitles... ▶ 00:00

(00:21) 4f Summary

(02:08) 5 Approximate Inference Sampling

(02:15) 6 Sampling Example

(01:05) 6a Sampling Example

(01:51) 6b More Sampling

(01:59) 7 Rejection Sampling

(01:55) 8 Likelihood Weighting

(00:37) 8a Answer

(00:20) 8b Likelihood Weighting is Consistent

(00:56) 8c Likelihood Weighting Problems

(01:50) 9 Gibbs Sampling

(01:19) 10 Monty Hall Problem

(01:45) 10a Answer

(00:44) 10b Monty Hall Letter

(12) Homework 2

(00:16) 1 Bayes Rule
[Thrun] Given the following Bayes network with P of A equal to 0.5, ▶ 00:00
P of B given the A equals 0.2, ▶ 00:06
and P of B given not A 0.8, ▶ 00:08
calculate the following probability. ▶ 00:12

(00:42) 2 Simple Bayes Net

(00:10) 3 Simple Bayes Net 2
[Thrun] Let us consider the same network again. ▶ 00:00
I would like to know the probability of X3 given that I observed X1. ▶ 00:03

(00:29) 4 Conditional Independence

(00:28) 5 Conditional Indepedence 2

(00:17) 6 Parameter Count

(00:36) 1 ANSWER

(03:16) 2 ANSWER

[Thrun] For this question we will be exploring a little trick	▶ 00:00
about non-normalized probability.	▶ 00:03
We will observe that P of A given X1, X2 and not X3,	▶ 00:05
the expression on the left can be resolved by Bayes' rule into this expression over here.	▶ 00:11
We will take X3 to the left and replace it by A,	▶ 00:16
both conditioned on the variables X1 and X2.	▶ 00:20
Then we have PA given X1, X2 divided by P not X3, X1, X2.	▶ 00:23
Next we employ 2 things.	▶ 00:29
One is the denominator does not depend on A,	▶ 00:31
so whether I put an A or not A has no bearing on any calculation here,	▶ 00:34
which means I can defer its calculation until later, and it will turn out to be important.	▶ 00:39
So I'm going to be proportional to just the stuff over here.	▶ 00:44
And second, I export my conditional independence	▶ 00:49
whereby I can omit X1 and X2 from the probability of not X3 conditioned on A.	▶ 00:52
These variables are conditionally independent.	▶ 00:58
This gives me the following recursion	▶ 01:02
where I now removed the third variable from the estimation problem	▶ 01:05
and just retained the first 2 relative to my initial expression.	▶ 01:10
If I keep expanding this, I get the following solution.	▶ 01:14
P of not X3 given A, P X2 given A, P X1 given A times P of A.	▶ 01:19
You might take a minute to just verify this,	▶ 01:27
but this is exploiting the conditional independence	▶ 01:30
very much as in the first step I showed you over here.	▶ 01:32
This step lacks the normalizer,	▶ 01:35
so let me work on the normalizer by expressing the opposite probability,	▶ 01:38
P of not A given the same events, X1, X2, and not X3,	▶ 01:44
which resolves to P of not X3 given not A,	▶ 01:50
P of X2 given not A, P of X1 given not A,	▶ 01:54
and P of not A.	▶ 02:00
I can now plug in the values from above.	▶ 02:02
So the first term gives me 0.8 times 0.2 times 0.2 times 0.5.	▶ 02:04
In the second term I get 0.4 times 0.6 times 0.6 times 0.5,	▶ 02:15
which resolves to 0.016 and 0.072.	▶ 02:24
This is clearly not a probability because we left out the normalizer.	▶ 02:31
But as we know, the normalizer does not depend on whether I put A or not A in here.	▶ 02:36
As a result, it will be the same for both of these expressions,	▶ 02:40
and I can obtain it by just adding these non-normalized probabilities	▶ 02:44
and then subsequently divide these non-normalized probabilities accordingly.	▶ 02:47
So let me just do this.	▶ 02:52
We get for the desired probability over here 0.1818	▶ 02:55
and for the inverse probability over here 0.8182.	▶ 03:01
Our desired answer therefore is 0.1818.	▶ 03:08
This was not an easy question.	▶ 03:14

(01:41) 3 ANSWER

(00:46) 4 ANSWER

(00:54) 5 ANSWER

(00:37) 6 ANSWER

(55) Unit 5

(01:11) 1 Introduction

(01:53) 2 What is Machine Learning

(00:47) 3 Answer

(01:33) 4 Stanley DARPA Grand Challenge

(03:46) 5 Taxonomy

Machine learning is a very large field	▶ 00:00
with many different methods	▶ 00:03
and many different applications.	▶ 00:04
I will now define some of the very basic terminology	▶ 00:06
that is being used to distinguish	▶ 00:10
different machine learning methods.	▶ 00:12
Let's start with the what.	▶ 00:13
What is being learned?	▶ 00:17
You can learn parameters	▶ 00:19
like the probabilities of a Bayes Network.	▶ 00:23
You can learn structure	▶ 00:26
like the arc structure of a Bayes Network.	▶ 00:27
And you might even discover hidden concepts.	▶ 00:31
For example	▶ 00:34
you might find that certain training example	▶ 00:35
form a hidden group.	▶ 00:37
For example Netflix	▶ 00:39
you might find that there's different types of customers	▶ 00:41
some that care about classic movies	▶ 00:43
some of them care about modern movies	▶ 00:45
and those might form hidden concepts	▶ 00:47
whose discovery can really help you	▶ 00:49
make better sense of the data.	▶ 00:51
Next is what from?	▶ 00:53
Every machine learning method	▶ 00:57
is driven by some sort of target information	▶ 01:00
that you care about.	▶ 01:02
In supervised learning	▶ 01:03
which is the subject of today's class	▶ 01:06
we're given specific target labels	▶ 01:08
and I give you examples just in a second.	▶ 01:10
We also talk about unsupervised learning	▶ 01:13
where target labels are missing	▶ 01:15
and we use replacement principles	▶ 01:19
to find, for example	▶ 01:21
hidden concepts.	▶ 01:22
Later there will be a class in reinforcement learning	▶ 01:24
when an agent learns from feedback with the physical environment	▶ 01:27
by interacting and trying actions	▶ 01:32
and receiving some sort of evaluation	▶ 01:34
from the environment	▶ 01:37
like "Well done" or "That works."	▶ 01:37
Again, we will talk about those in detail later.	▶ 01:41
There's different things you could try to do	▶ 01:43
with machine learning technique.	▶ 01:46
You might care about prediction.	▶ 01:48
For example you might want to care about what's going to happen with the future	▶ 01:49
in the stockmarket for example.	▶ 01:53
You might care to diagnose something	▶ 01:55
which is you get data and you wish to explain it	▶ 01:57
and you use machine learning for that.	▶ 01:59
Sometimes your objective is to summarize something.	▶ 02:01
For example if you read a long article	▶ 02:04
your machine learning method might aim to	▶ 02:07
produce a short article that summarizes the long article.	▶ 02:09
And there's many, many, many more different things.	▶ 02:12
You can talk about the how to learn.	▶ 02:14
We use the word passive	▶ 02:16
if your learning agent is just an observer	▶ 02:19
and has no impact on the data itself.	▶ 02:23
Otherwise, you call it active.	▶ 02:24
Sometimes learning occurs online	▶ 02:26
which means while the data is being generated	▶ 02:30
and some of it is offline	▶ 02:32
which means learning occurs	▶ 02:35
after the data has been generated.	▶ 02:37
There's different types of outputs	▶ 02:39
of a machine learning algorithm.	▶ 02:42
Today we'll talk about classification	▶ 02:44
versus regression.	▶ 02:47
In classification the output is binary	▶ 02:50
or a fixed number of classes	▶ 02:53
for example something is either a chair or not.	▶ 02:55
Regression is continuous.	▶ 02:57
The temperature might be 66.5 degrees	▶ 02:59
in our prediction.	▶ 03:01
And there's tons of internal details	▶ 03:03
we will talk about.	▶ 03:05
Just to name one.	▶ 03:07
We will distinguish generative	▶ 03:09
from discriminative.	▶ 03:12
Generative seeks to model the data	▶ 03:14
as generally as possible	▶ 03:16
versus discriminative methods	▶ 03:18
seek to distinguish data	▶ 03:20
and this might sound like a superficial distinction	▶ 03:21
but it has enormous ramification	▶ 03:24
on the learning algorithm.	▶ 03:26
Now to tell you the truth	▶ 03:27
it took me many years	▶ 03:29
to fully learn all these words here	▶ 03:30
and I don't expect you to pick them all up	▶ 03:33
in one class	▶ 03:36
but you should as well know that they exist.	▶ 03:37
And as they come up	▶ 03:39
I'll emphasize them	▶ 03:41
so you can resort any learning method	▶ 03:42
I tell you back into the specific taxonomy over here.	▶ 03:44

(03:12) 6 Supervised Learning

The vast amount of work in the field	▶ 00:00
falls into the area of supervised learning.	▶ 00:02
In supervised learning	▶ 00:06
you're given for each training example	▶ 00:08
a feature vector	▶ 00:10
and a target label named Y.	▶ 00:13
For example, for a credit rating agency	▶ 00:16
X1, X2, X3 might be a feature	▶ 00:20
such as is the person employed?	▶ 00:23
What is the salary of the person?	▶ 00:25
Has the person previously defaulted on a credit card?	▶ 00:27
And so on.	▶ 00:30
And Y is a predictor	▶ 00:32
whether the person is to default	▶ 00:34
on the credit or not.	▶ 00:36
Now machine learning	▶ 00:38
is to be carried out on past data	▶ 00:40
where the credit rating agency	▶ 00:42
might have collected features just like these	▶ 00:44
and actual occurances of default or not.	▶ 00:46
What it wishes to produce	▶ 00:49
is a function that allows us	▶ 00:51
to predict future customers.	▶ 00:53
So the new person comes in	▶ 00:55
with a different feature vector.	▶ 00:56
Can we predict as good as possible	▶ 00:58
the functional relationship	▶ 01:00
between these features X1 to Xn all the way to Y?	▶ 01:02
You can apply the exact same example	▶ 01:05
in image recognition	▶ 01:08
where X might be pixels of images	▶ 01:09
or it might be features of things found in images	▶ 01:11
and Y might be a label that says	▶ 01:14
whether a certain object is contained	▶ 01:16
in an image or not.	▶ 01:17
Now in supervised learning	▶ 01:19
you're given many such examples.	▶ 01:20
X21 to X2n	▶ 01:25
leads to Y2	▶ 01:28
all way the index m.	▶ 01:32
This is called your data.	▶ 01:35
If we call each input vector Xm	▶ 01:38
and we wish to find out the function	▶ 01:43
given any Xm or any future vector X	▶ 01:44
produces as close as possible	▶ 01:50
my target signal Y.	▶ 01:53
Now this isn't always possible	▶ 01:55
and sometimes it's acceptable	▶ 01:57
in fact preferable	▶ 01:59
to tolerate a certain amount of error	▶ 02:00
in your training data.	▶ 02:03
But the subject of machine learning	▶ 02:05
is to identify this function over here.	▶ 02:07
And once you identify it	▶ 02:10
you can use it for future Xs	▶ 02:11
that weren't part of the training set	▶ 02:13
to produce a prediction	▶ 02:16
that hopefully is really, really good.	▶ 02:19
So let me ask you a question.	▶ 02:21
And this is a question	▶ 02:24
for which I haven't given you the answer	▶ 02:27
but I'd like to appeal to your intuition.	▶ 02:28
Here's one data set	▶ 02:31
where the X is one dimensionally plotted horizontally	▶ 02:34
and the Y is vertically	▶ 02:37
and suppose there looks like this.	▶ 02:39
Suppose my machine learning algorithm	▶ 02:44
gives me 2 hypotheses.	▶ 02:45
One is this function over here	▶ 02:47
which is a linear function	▶ 02:51
and one is this function over here.	▶ 02:52
I'd like to know which of the functions	▶ 02:53
you find preferable	▶ 02:57
as an explanation for the data.	▶ 02:59
Is it function A?	▶ 03:01
Or function B?	▶ 03:02
Check here for A	▶ 03:06
here for B	▶ 03:08
and here for neither.	▶ 03:09

(02:43) 7 Occam's Razor

And I hope you guessed function A.	▶ 00:00
Even though both perfectly describe the data	▶ 00:04
B is much more complex than A.	▶ 00:08
In fact, outside the data	▶ 00:10
B seems to go to a minus infinity much faster	▶ 00:12
than these data points	▶ 00:16
and to plus infinity much faster	▶ 00:17
with these data points over here.	▶ 00:19
And in between	▶ 00:21
we have wide oscillations	▶ 00:22
that don't correspond to any data.	▶ 00:23
So I would argue	▶ 00:25
A is preferable.	▶ 00:27
The reason why I asked this question	▶ 00:31
is because of something called Occam's Razor.	▶ 00:32
Occam can be spelled in many different ways.	▶ 00:35
And what Occam says is that	▶ 00:38
everything else being equal	▶ 00:41
chose the less complex hypothesis.	▶ 00:43
Now in practice	▶ 00:46
there's actually a trade-off	▶ 00:48
between a really good data fit	▶ 00:50
and low complexity.	▶ 00:53
Let me illustrate this to you	▶ 00:55
by a hypothetical example.	▶ 00:58
Consider the following graph	▶ 00:59
where the horizontal axis graphs	▶ 01:02
complexity of the solution.	▶ 01:04
For example, if you use polynomials	▶ 01:07
this might be a high-degree polynomial over here	▶ 01:10
and maybe a linear function over here	▶ 01:12
which is a low-degree polynomial	▶ 01:14
your training data error	▶ 01:16
tends to go like this.	▶ 01:19
The more complex the hypothesis you allow	▶ 01:22
the more you can just fit your data.	▶ 01:25
However, in reality	▶ 01:29
your generalization error on unknown data	▶ 01:31
tends to go like this.	▶ 01:33
It is the sum of the training data error	▶ 01:37
and another function	▶ 01:40
which is called the overfitting error.	▶ 01:42
Not surprisingly	▶ 01:46
the best complexity is obtained	▶ 01:47
where the generalization error is minimum.	▶ 01:49
There are methods	▶ 01:52
to calculate the overfitting error.	▶ 01:53
They go into a statistical field	▶ 01:55
under the name Bayes variance methods.	▶ 01:57
However, in practice	▶ 02:01
you're often just given the training data error.	▶ 02:02
You find if you don't find the model	▶ 02:04
that minimizes the training data error	▶ 02:08
but instead pushes back the complexity	▶ 02:11
your algorithm tends to perform better	▶ 02:14
and that is something we will study a little bit	▶ 02:17
in this class.	▶ 02:20
However, this slide is really important	▶ 02:22
for anybody doing machine learning in practice.	▶ 02:26
If you deal with data	▶ 02:29
and you have ways to fit your data	▶ 02:31
be aware that overfitting	▶ 02:33
is a major source of poor performance	▶ 02:36
of a machine learning algorithm.	▶ 02:39
And I give you examples in just one second.	▶ 02:41

(04:15) 8 SPAM Detection

So a really important example	▶ 00:00
of machine learning is SPAM detection.	▶ 00:02
We all get way too much email	▶ 00:04
and a good number of those are SPAM.	▶ 00:06
Here are 3 examples of email.	▶ 00:08
Dear Sir: First I must solicit your confidence	▶ 00:12
in this transaction, this is by virtue of its nature	▶ 00:14
being utterly confidential and top secret...	▶ 00:16
This is likely SPAM.	▶ 00:19
Here's another one.	▶ 00:22
In upper caps.	▶ 00:23
99 MILLION EMAIL ADDRESSES FOR ONLY $99	▶ 00:25
This is very likely SPAM.	▶ 00:28
And here's another one.	▶ 00:31
Oh, I know it's blatantly OT	▶ 00:33
but I'm beginning to go insane.	▶ 00:35
Had an old Dell Dimension XPS sitting in the corner	▶ 00:37
and decided to put it to use.	▶ 00:40
And so on and so on.	▶ 00:41
Now this is likely not SPAM.	▶ 00:42
How can a computer program	▶ 00:45
distinguish between SPAM and not SPAM?	▶ 00:47
Let's use this as an example	▶ 00:49
to talk about machine learning for discrimination	▶ 00:51
using Bayes Networks.	▶ 00:55
In SPAM detection	▶ 00:59
we get an email	▶ 01:01
and we wish to categorize it	▶ 01:03
either as SPAM	▶ 01:05
in which case we don't even show as to the where	▶ 01:07
or what we call HAM	▶ 01:10
which is the technical word for	▶ 01:12
an email worth passing on to the person being emailed.	▶ 01:15
So the function over here	▶ 01:19
is the function we're trying to learn.	▶ 01:21
Most SPAM filters use human input.	▶ 01:23
When you go through email	▶ 01:26
you have a button called IS SPAM	▶ 01:28
which allows you as a user to flag SPAM	▶ 01:32
and occasionally you will say an email is SPAM.	▶ 01:34
If you look at this	▶ 01:37
you have a typical supervised machine learning situation	▶ 01:40
where the input is an email	▶ 01:43
and the output is whether you flag it as SPAM	▶ 01:45
or if we don't flag it	▶ 01:47
we just think it's HAM.	▶ 01:49
Now to make this amenable to	▶ 01:52
a machine learning algorithm	▶ 01:54
we have to talk about how to represent emails.	▶ 01:55
They're all using different words and different characters	▶ 01:57
and they might have different graphics included.	▶ 02:00
Let's pick a representation that's easy to process.	▶ 02:02
And this representation is often called	▶ 02:06
Bag of Words.	▶ 02:09
Bag of Words is a representation	▶ 02:10
of a document	▶ 02:14
that just counts the frequency	▶ 02:15
of words.	▶ 02:17
If an email were to say Hello	▶ 02:18
I will say Hello.	▶ 02:22
The Bag of Words representation	▶ 02:24
is the following.	▶ 02:26
2-1-1-1	▶ 02:27
for the dictionary	▶ 02:31
that contains the 4 words	▶ 02:33
Hello I will say.	▶ 02:36
Now look at the subtlety here.	▶ 02:38
Rather than representing each individual word	▶ 02:41
we have a count of each word	▶ 02:43
and the count is oblivious	▶ 02:46
to the order in which the words were stated.	▶ 02:49
A Bag of Words representation	▶ 02:52
relative to a fixed dictionary	▶ 02:55
represents the counts of each word	▶ 02:57
relative to the words in the dictionary.	▶ 03:01
If you were to use a different dictionary	▶ 03:03
like hello and good-bye	▶ 03:06
our counts would be	▶ 03:08
2 and 0.	▶ 03:10
However, in most cases	▶ 03:13
you make sure that all the words found	▶ 03:14
in messages	▶ 03:17
are actually included in the dictionary.	▶ 03:18
So the dictionary might be very, very large.	▶ 03:19
Let me make up an unofficial example	▶ 03:22
of a few SPAM and a few HAM messages.	▶ 03:25
Offer is secret.	▶ 03:30
Click secret link.	▶ 03:32
Secret sports link.	▶ 03:35
Obviously those are contrived	▶ 03:37
and I tried to retain the recovery	▶ 03:40
to a small number of words	▶ 03:42
to make this example workable.	▶ 03:44
In practice we need thousands	▶ 03:46
of such messages	▶ 03:47
to get good information.	▶ 03:48
Play sports today.	▶ 03:50
Went play sports.	▶ 03:52
Secret sports event.	▶ 03:54
Sport is today.	▶ 03:56
Sport costs money.	▶ 03:59
My first quiz is	▶ 04:02
What is the size of the vocabulary	▶ 04:06
that contains all words in these messages?	▶ 04:08
Please enter the value in this box over here.	▶ 04:12

(00:28) 9 Answer

(00:16) 10 Question

(00:16) 11 Answer
[Narrator] And the answer is: ▶ 00:00
there's 8 different messages ▶ 00:02
of which 3 are spam. ▶ 00:04
So the maximum likelihood estimate ▶ 00:06
is 3/8. ▶ 00:09
[writing on paper] ▶ 00:11

(04:31) 12 Maximum Likelihood

So, let's look at this a little bit more formally and talk about maximum likelihood.	▶ 00:00
Obviously, we're observing 8 messages: spam, spam, spam, and 5 times ham.	▶ 00:03
And what we care about is what's our prior probability of spam	▶ 00:12
that maximizes the likelihood of this data?	▶ 00:17
So, let's assume we're going to assign a value of pi to this,	▶ 00:20
and we wish to find the pi that maximizes the likelihood of this data over here,	▶ 00:24
assuming that each email is drawn independently	▶ 00:29
according to an identical distribution.	▶ 00:33
The probability of the p(yi) data item is then pi if yi = spam,	▶ 00:37
and 1 - pi if yi = ham.	▶ 00:48
If we rewrite the data as 1, 1, 1, 0, 0, 0, 0, 0,	▶ 00:53
we can write p(yi) as follows: pi to the yi times (1 - pi) to the 1 - yi.	▶ 00:59
It's not that easy to see that this is equivalent,	▶ 01:13
but say yi = 1.	▶ 01:16
Then this term will fall out.	▶ 01:19
It's not proficient by 1 because the exponent is zero, and we get pi as over here.	▶ 01:22
If yi = 0, then this term falls out, and this one here becomes 1 - pi as over here.	▶ 01:28
Now assuming independence, we get for the entire data set	▶ 01:36
that the joint probability of all data items is the product	▶ 01:44
of the individual data items over here,	▶ 01:49
which can now be written as follows:	▶ 01:52
pi to the count of instances where yi = 1 times	▶ 01:56
1 - pi to the count of the instances where yi = 0.	▶ 02:03
And we know in our example, this count over here is 3,	▶ 02:09
and this count over here is 5, so we get pi to the 3rd times 1 - pi to the 5th.	▶ 02:13
We now wish to find the pi that maximizes this expression over here.	▶ 02:22
We can also maximize the logarithm of this expression,	▶ 02:28
which is 3 times log pi + 5 times log (1 - pi)	▶ 02:33
Optimizing the log is the same as optimizing p because the log is monotonic to p.	▶ 02:42
The maximum of this function is attained with a derivative of 0,	▶ 02:50
so let's compute with a derivative and set it to 0.	▶ 02:54
This is the derivative, 3 over pi - 5 over 1 - pi.	▶ 03:00
We now bring this expression to the right side,	▶ 03:05
multiply the denominators up, and sort all the expressions containing pi to the left,	▶ 03:09
which gives us pi = 3/8, exactly the number we were at before.	▶ 03:18
We just derived mathematically that the data likelihood maximizing number	▶ 03:26
for the probability is indeed the empirical count,	▶ 03:33
which means when we looked at this quiz before	▶ 03:37
and we said a maximum likelihood for the prior probability of spam is 3/8,	▶ 03:41
by simply counting 3 over 8 emails were spam,	▶ 03:49
we actually followed proper mathematical principles	▶ 03:54
to do maximum likelihood estimation.	▶ 03:57
Now, you might not fully have gotten the derivation of this,	▶ 03:59
and I recommend you to watch it again, but it's not that important	▶ 04:03
for the progress in this class.	▶ 04:07
So, here's another quiz.	▶ 04:09
I'd like the maximum likelihood, or ML solutions,	▶ 04:11
for the following probabilities.	▶ 04:17
The probability that the word "secret" comes up,	▶ 04:19
assuming that we already know a message is spam,	▶ 04:21
and the probability that the same word "secret" comes up	▶ 04:25
if we happen to know the message is not spam, it's ham.	▶ 04:28

(00:25) 13 Answer

(01:19) 14 Relationship to Bayes Networks

(00:29) 15 Answer

(00:26) 16 Question

(01:02) 17 Answer

(00:21) 18 Question

(01:03) 19 Answer

(00:21) 20 Question

(03:19) 21 Answer and Laplace Smoothing

And surprisingly, the probability for this message to be spam is 0.	▶ 00:00
It's not 0.001. It's flat 0.	▶ 00:07
In other words, it's impossible, according to our model,	▶ 00:11
that this text could be a spam message.	▶ 00:14
Why is this?	▶ 00:17
When we apply the same rule as before, we get the prior for spam which is 3/8.	▶ 00:19
And we multiple the conditional for each word into this.	▶ 00:24
For "secret," we know it to be 1/3.	▶ 00:28
For "is," to be 1/9, but for today, it's 0.	▶ 00:31
It's 0 because the maximum of the estimate for the probability of "today" in spam is 0.	▶ 00:39
"Today" just never occurred in a spam message so far.	▶ 00:45
Now, this 0 is troublesome because as we compute the outcome--	▶ 00:49
and I'm plugging in all the numbers as before--	▶ 00:55
none of the words matter anymore, just the 0 matters.	▶ 01:00
So, we get 0 over something which is plain 0.	▶ 01:03
Are we overfitting? You bet.	▶ 01:10
We are clearly overfitting.	▶ 01:13
It can't be that a single word determines the entire outcome of our analysis.	▶ 01:15
The reason is that our model, to assign a probability of 0 for the word "today"	▶ 01:21
to be in the class of spam is just too aggressive.	▶ 01:26
Let's change this.	▶ 01:29
One technique to deal with the overfitting problem is called Laplace smoothing.	▶ 01:34
In maximum likelihood estimation, we assign towards our probability	▶ 01:39
the quotient of the count of this specific event over all events in our data set.	▶ 01:45
For example, for the prior probability, we found that 3/8 messages are spam.	▶ 01:51
Therefore, our maximum likelihood estimate	▶ 01:57
for the prior probability of spam was 3/8.	▶ 02:00
In Laplace Smoothing, we use a different estimate.	▶ 02:05
We add the value k to the count	▶ 02:10
and normalize as if we added k to every single class	▶ 02:15
that we've tried to estimate something over.	▶ 02:20
This is equivalent to assuming we have a couple of fake training examples	▶ 02:23
where we add k to each observation count.	▶ 02:28
Now, if k equals 0, we get our maximum likelihood estimator.	▶ 02:32
But if k is larger than 0 and n is finite, we get different answers.	▶ 02:36
Let's say k equals 1,	▶ 02:41
and let's assume we get one message,	▶ 02:47
and that message was spam, so we're going to write it one message, one spam.	▶ 02:51
What is p (spam) for the Laplace smoothing of k + 1?	▶ 02:56
Let's do the same with 10 messages, and we get 6 spam.	▶ 03:03
And 100 messages, of which 60 are spam.	▶ 03:09
Please enter your numbers into the boxes over here.	▶ 03:16

(01:14) 22 Answer

(00:25) 23 Question

(01:17) 24 Answer

(00:21) 25 Question

(00:58) 26 Answer

(01:47) 27 Summary Naive Bayes

(01:27) 28 Advanced SPAM Filtering

(02:21) 29 Digit Recognition

[Narrator] Naive Bayes can also be applied to	▶ 00:00
the problem of hand written digits recognition.	▶ 00:02
This is a sample of hand-written digits taken	▶ 00:05
from a U.S. postal data set	▶ 00:09
where hand written zip codes on letters are	▶ 00:12
being scanned and automatically classified.	▶ 00:17
The machine-learning problem here is	▶ 00:21
taken a symbol just like this.	▶ 00:23
What is the corresponding number?	▶ 00:28
Here it's obviously 0.	▶ 00:30
Here it's obviously 1.	▶ 00:32
Here it's obviously 2, 1.	▶ 00:34
For the one down here,	▶ 00:36
it's a little bit harder to tell.	▶ 00:38
Now when you apply Naive Bayes,	▶ 00:41
the input vector	▶ 00:44
could be the pixel values	▶ 00:46
of each individual pixel so we have	▶ 00:48
a 16 x 16 input resolution.	▶ 00:50
You would get 256 different values	▶ 00:54
corresponding to the brightness of each pixel.	▶ 00:59
Now obviously given sufficiently made	▶ 01:02
training example, you might hope	▶ 01:05
to recognize digits,	▶ 01:07
but one of the deficiencies of this approach is	▶ 01:09
it is not particularly shifted range.	▶ 01:12
So for example a pattern like this	▶ 01:15
will look fundamentally different	▶ 01:19
from a pattern like this.	▶ 01:21
Even though the pattern on the right is obtained	▶ 01:24
by shifting the pattern on the left	▶ 01:27
by 1 to the right.	▶ 01:29
There's many different solutions, but a common one could be	▶ 01:31
to use smoothing in a different way from	▶ 01:34
the way we discussed it before.	▶ 01:36
Instead of just counting 1 pixel value's count,	▶ 01:38
you could mix it with counts of the	▶ 01:40
neighboring pixel values so if	▶ 01:42
all pixels are slightly shifted,	▶ 01:44
we get about the same statistics	▶ 01:46
as the pixel itself.	▶ 01:48
Such a method is called input smoothing.	▶ 01:50
You can what's technically called convolve	▶ 01:52
the input vector equals pixel value variable, and	▶ 01:55
you might get better results than if you	▶ 01:57
do Naive Bayes on the raw pixels.	▶ 02:00
Now to tell you the truth for	▶ 02:02
digit recognition of this type,	▶ 02:04
Naive Bayes is not a good choice.	▶ 02:06
The conditional independence assumption	▶ 02:08
of each pixel, given the class,	▶ 02:10
is too strong an assumption in this case,	▶ 02:12
but it's fun to talk about image recognition	▶ 02:14
in the context of Naive Bayes regardless.	▶ 02:17

(03:30) 30 Overfitting Prevention

So, let me step back a step and talk a bit about	▶ 00:00
overfitting prevention in machine learning	▶ 00:04
because it's such an important topic.	▶ 00:07
We talked about Occam's Razor,	▶ 00:09
which in a generalized way suggests there is	▶ 00:12
a tradeoff between how well we can fit the data	▶ 00:16
and how smooth our learning algorithm is.	▶ 00:22
In our class in smoothing, we already found 1 way	▶ 00:28
to let Occam's Razor play, which is by	▶ 00:32
selecting the value K to make our statistical counts smoother.	▶ 00:34
I alluded to a similar way in the image recognition domain	▶ 00:40
where we smoothed the image so the neighboring pixels count similar.	▶ 00:44
This all raises the question of how to choose the smoothing parameter.	▶ 00:49
So, in particular, in Laplacian smoothing, how to choose the K.	▶ 00:53
There is a method called cross-validation	▶ 00:58
which can help you find an answer.	▶ 01:02
This method assumes there is plenty of training examples, but	▶ 01:05
to tell you the truth, in spam filtering there is more than you'd ever want.	▶ 01:09
Take your training data	▶ 01:14
and divide it into 3 buckets.	▶ 01:17
Train, cross-validate, and test.	▶ 01:19
Typical ratios will be 80% goes into train,	▶ 01:24
10% into cross-validate,	▶ 01:27
and 10% into test.	▶ 01:30
You use the train to find all your parameters.	▶ 01:33
For example, the probabilities of a base network.	▶ 01:37
You use your cross-validation set	▶ 01:40
to find the optimal K, and the way you do this is	▶ 01:43
you train for different values of K,	▶ 01:46
you observe how well the training model performs on the CV data,	▶ 01:49
not touching the test data,	▶ 01:55
and then you maximize over all the Ks to get the best performance	▶ 01:58
on the cross-validation set.	▶ 02:01
You iterate this many times until you find the best K.	▶ 02:03
When you're done with the best K,	▶ 02:06
you train again, and then finally	▶ 02:09
only one you touch the test data	▶ 02:12
to verify the performance,	▶ 02:15
and this is the performance you report.	▶ 02:17
It's really important in cross-validation	▶ 02:20
split apart a cross-validation set that's different from the test set.	▶ 02:23
If you were to use the test set to find the optimal K,	▶ 02:28
then your test set becomes an effective part of your training routine,	▶ 02:31
and you might overfit your test data,	▶ 02:35
and you wouldn't even know.	▶ 02:38
By keeping the test data separate from the beginning,	▶ 02:40
and train on the training data, you use	▶ 02:43
the cross-validation data to find how good your train data is doing,	▶ 02:46
and the unknown parameters of K to fine-tune the K.	▶ 02:49
Finally, only once you use the test data	▶ 02:53
do you get a fair answer to the question,	▶ 02:56
"How well will your model perform on future data?"	▶ 02:59
So, pretty much everybody in machine learning	▶ 03:02
uses this model.	▶ 03:05
You can redo the split between training and the cross-validation part,	▶ 03:08
people often use the word 10-fold cross-validation	▶ 03:12
where they do 10 different forwardings	▶ 03:15
and run the model 10 times to find the optimal K	▶ 03:17
or smoothing parameter.	▶ 03:20
No matter which way you do it, find the optimal smoothing parameter	▶ 03:22
and then use a test set exactly once to verify in a report.	▶ 03:25

(02:00) 31 Classification vs Regression

(00:26) 32 Answer

(02:46) 33 Linear Regression

Now obviously you can answer this question without understanding anything about regression.	▶ 00:00
But what you find is this is different from classification as before.	▶ 00:05
This is not a binary concept anymore of like expensive and cheap.	▶ 00:10
It really is a relationship between two variables.	▶ 00:13
One you care about--the house price, and one that you can observe,	▶ 00:17
which is the house size in square feet.	▶ 00:20
And your goal is to fit a curve that best explains the data.	▶ 00:23
Once again, we have a case where we can play Occam's razor.	▶ 00:28
There clearly is a data fit that is not linear that might be better,	▶ 00:31
like this one over here.	▶ 00:35
And when you go to hide the linear curves,	▶ 00:37
you might even be inclined to draw a curve like this.	▶ 00:40
Now of course the curve I'm drawing right now is likely an overfit.	▶ 00:44
And you don't want to postulate that this is the general relationship	▶ 00:49
between the size of a house and the sales price.	▶ 00:54
So even though my black curve might describe the data better,	▶ 00:57
the blue curve or the dashed linear curve over here might be a better explanation overture of Occam's razor.	▶ 01:01
So let's look a little bit deeper into what we call regression.	▶ 01:08
As in all regression problems, our data will be comprised of	▶ 01:15
input vectors of length in that map to another continuous value.	▶ 01:19
And we might be given a total of M data points.	▶ 01:25
This is from the classification case, except this time the Ys are continuous.	▶ 01:30
Once again, we're looking for function f that maps our vector x into y.	▶ 01:36
In linear regression, the function has a particular form which is W1 times X plus W0.	▶ 01:44
In this case X is one dimensional which is N = 1.	▶ 01:54
Or in the high-dimensional space, we might just write W times X plus W0,	▶ 01:59
where W is a vector and X is a vector.	▶ 02:07
And this is the inner product of these 2 vectors over here.	▶ 02:12
Let's for now just consider the one-dimensional case.	▶ 02:16
In this quiz, I've given you a linear regression form with 2 unknown parameters, W1 and W0.	▶ 02:20
I've given you a data set.	▶ 02:27
And this data set happens to be fittable by a linear regression model without any residual error.	▶ 02:30
Without any math, can you look at this and find out to me what the 2 parameters, W0 and W1 are?	▶ 02:36

(01:17) 34 Answer

(01:00) 35 More Linear Regression

(04:04) 36 Quadratic Loss

The problem of minimizing quadratic loss for linear functions can be solved in closed form.	▶ 00:00
When I reduce, I will do this for the one-dimensional case on paper.	▶ 00:07
I will also give you the solution for the case where your input space is multidimensional,	▶ 00:12
which is often called "multivariant regression."	▶ 00:17
We seek to minimize a sum of a quadratic expression	▶ 00:22
where the target labels are subtracted with the output of our linear regression model	▶ 00:26
parameterized by w1 and w2.	▶ 00:33
The summation here is overall training examples,	▶ 00:36
and I leave the index of the summation out if not necessary.	▶ 00:40
The minimum of this is obtained where the derivative of this function equals zero.	▶ 00:45
Let's call this function "L."	▶ 00:50
For the partial derivative with respect to w0, we get this expression over here,	▶ 00:53
which we have to set to zero.	▶ 00:59
We can easily get rid of the -2 and transform this as follows:	▶ 01:02
Here M is the number of training examples.	▶ 01:11
This expression over here gives us w0 as a function of w1,	▶ 01:17
but we don't know w1. Let's do the same trick for w1	▶ 01:21
and set this to zero as well,	▶ 01:28
which gets us the expression over here.	▶ 01:32
We can now plug in the w0 over here into this expression over here	▶ 01:38
and obtain this expression over here,	▶ 01:44
which looks really involved but is relatively straightforward.	▶ 01:47
With a few steps of further calculation, which I'll spare you for now,	▶ 01:52
we get for w1 the following important formula:	▶ 01:56
This is the final quotient for w1,	▶ 02:02
where we take the number of training examples times of the sum of all xy	▶ 02:05
minus the sum of x times the sum of y divided by this expression over here.	▶ 02:10
Once we've computed w1,	▶ 02:16
we can go back to our original articulation of w0 over here	▶ 02:19
and plug w1 into w0 and obtain w0.	▶ 02:23
These are the two important formulas we can also find in the textbook.	▶ 02:30
I'd like to go back and use those formulas to calculate these two coefficients over here.	▶ 02:39
You get 4 times the sum of x and the sum of y, which is -32	▶ 02:45
minus the product of the sum of x, which is 18, and the sum of y, which is -6,	▶ 02:56
divided by the sum of x squared, which is 86, times 4, minus the sum of x squared,	▶ 03:05
which is 18 times 18, which is 324.	▶ 03:16
If you work this all out, it becomes -1, which is w1.	▶ 03:20
W0 is now obtained by completing the quarter times sum of all y,	▶ 03:25
which is -6, minus -1/4 times sum of all x.	▶ 03:31
If you plug this all in, you get 3, as over here. Our formula is actually correct.	▶ 03:39
Here is another quiz for linear regression. We have the follow data:	▶ 03:46
Here is the data plotted graphically.	▶ 03:51
I wonder what the best regression is.	▶ 03:53
Give me w0 and w1. Apply the formulas I just gave you.	▶ 03:56

(01:27) 37 Answer

(02:10) 38 Problems with Linear Regression

So linear regression works well	▶ 00:00
if the data is approximately linear,	▶ 00:03
but there are many examples when linear regression performs poorly.	▶ 00:05
Here's one where we have a	▶ 00:09
curve that is really nonlinear.	▶ 00:12
This is an interesting one where we seem to have a linear relationship	▶ 00:15
that is flatter than the linear regression indicates,	▶ 00:18
but there is one outlier.	▶ 00:21
Because if you are minimizing quadratic error,	▶ 00:23
outliers penalize you over-proportionately.	▶ 00:26
So outliers are particularly bad for linear regression.	▶ 00:30
And here is a case,	▶ 00:34
where the data clearly suggests	▶ 00:35
a very different phenomena for linear.	▶ 00:37
We have only two ?? variables even being used,	▶ 00:40
and this one has a strong frequency	▶ 00:42
and a strong vertical spread.	▶ 00:45
Clearly a linear regression model	▶ 00:47
is a very poor one to explain	▶ 00:49
this data over here.	▶ 00:51
Another problem with linear regression	▶ 00:53
is that as you go to infinity in the X space,	▶ 00:55
your Ys also become infinite.	▶ 00:59
In some problems that isn't a plausible model.	▶ 01:02
For example, if you wish to predict the weather	▶ 01:05
anytime into the future,	▶ 01:08
it's implausible to assume the further the prediction goes out,	▶ 01:10
the hotter or the cooler it becomes.	▶ 01:13
For such situations there is a	▶ 01:15
model called logistic regression,	▶ 01:17
which uses a slightly more complicated	▶ 01:20
model than linear regression,	▶ 01:22
which goes as follows:.	▶ 01:24
Let F of XP, or linear function,	▶ 01:25
and the output of logistic regression	▶ 01:30
is obtained by the following function:	▶ 01:32
One over one plus exponential of minus F of X.	▶ 01:34
So here's a quick quiz for you.	▶ 01:40
What is the range in which Z might fall	▶ 01:43
given this function over here,	▶ 01:48
and ??? the linear function of F or X over here.	▶ 01:49
Is it zero, one?	▶ 01:53
Is it minus one, one?	▶ 01:56
Is it minus one, zero?	▶ 01:59
Minus two, two?	▶ 02:02
Or none of the above?	▶ 02:04

(01:00) 39 Answer

(01:39) 40 Linear Regression and Complexity Control

(01:46) 41 Minimizing Complicated Loss Functions

(00:47) 42 Answer

(00:28) 43 Question

(00:20) 44 Answer

(00:21) 45 Question

(01:00) 46 Answer

(01:41) 47 Gradient Descent Implementation

(04:15) 48 Perceptron

Now, there are many different ways to apply linear functions in machine learning.	▶ 00:00
We so far have studied linear functions for regression,	▶ 00:08
but linear functions are also used for classification,	▶ 00:12
and specifically for an algorithm called the perceptron algorithm.	▶ 00:16
This algorithm happens to be a very early model of a neuron,	▶ 00:21
as in the neurons we have in our brains,	▶ 00:27
and was invented in the 1940s.	▶ 00:30
Suppose we give a data set of positive samples and negative samples.	▶ 00:33
A linear separator is a linear equation that separates positive from negative examples.	▶ 00:41
Obviously, not all sets possess a linear separator, but some do.	▶ 00:49
For those we can define the algorithm of the perceptron and it actually converges.	▶ 00:55
To define a linear separator, let's start with our linear equation as before--	▶ 01:02
w1x + w0 in cases where x is higher dimensional this might actually be a vector--never mind.	▶ 01:07
If this is larger or equal to zero, then we call our classification 1.	▶ 01:18
Otherwise, we call it zero.	▶ 01:26
Here's our linear separation classification function	▶ 01:30
where this is our common linear function.	▶ 01:35
Now, as I said, perceptron only converges if the data is linearly separable,	▶ 01:39
and then it converges to a linear separation of the data,	▶ 01:45
which is quite amazing.	▶ 01:49
Perceptron is an iterative algorithm that is not dissimilar from grade descent.	▶ 01:52
In fact, the update rule echoes that of grade descent, and here's how it goes.	▶ 01:56
We start with a random guess for w1 and w0,	▶ 02:03
which may correspond to a random separation line,	▶ 02:09
but usually is inaccurate.	▶ 02:13
Then the mth weight-i is obtained by using the old weight plus some learning rate alpha	▶ 02:17
times the difference between the desired target label	▶ 02:29
and the target label produced by our function at the point m-1.	▶ 02:33
Now, this is an online learning rule, which is we don't process all the data in batch.	▶ 02:39
We process one data at a time, and we might go through the data many, many times--	▶ 02:45
hence the j over here--	▶ 02:50
but every time we do this, we apply this rule over here.	▶ 02:52
What this rule gives us is a method to adapt our weights in proportion to the error.	▶ 02:55
If the prediction of our function f equals our target label,	▶ 03:03
and the error is zero, then no update occurs.	▶ 03:07
If there is a difference, however, we update in a way so as to minimize the error.	▶ 03:11
Alpha is a small learning weight.	▶ 03:18
Once again, perceptron converges to a correct linear separator	▶ 03:22
if such linear separator exists.	▶ 03:28
Now, the case of linear separation has recently received a lot of attention in machine learning.	▶ 03:31
If you look at the picture over here, you'll find there are many different linear separators.	▶ 03:36
There is one over here. There is one over here. There is one over here.	▶ 03:42
One of the questions that has recently been researched extensively is which one to prefer.	▶ 03:47
Is it a, b, or c?	▶ 03:53
Even though you probably have never seen this literature,	▶ 03:57
I will just ask your intuition in this following quiz.	▶ 04:01
Which linear separator would you prefer if you look at these three different linear separators--	▶ 04:05
a, b, c, or none of them?	▶ 04:10

(05:38) 49 Answer and SVMs

[Narrator] And intuitively I would argue it's B,	▶ 00:00
and the reason why is	▶ 00:04
C comes really close to examples.	▶ 00:06
So if these examples are noisy,	▶ 00:09
it's quite likely that	▶ 00:12
by being so close to these examples	▶ 00:14
that future examples cross the line.	▶ 00:17
Similarly A comes close to examples.	▶ 00:20
B is the one that stays really far away	▶ 00:23
from any example.	▶ 00:26
So there's this entire region over here	▶ 00:28
where there's no example anywhere near B.	▶ 00:31
This region is often called the margin.	▶ 00:34
The margin of the linear separator	▶ 00:37
is the distance of the separator	▶ 00:40
to the closest training example.	▶ 00:43
The margin is a really important concept	▶ 00:45
in machine learning.	▶ 00:47
There is an entire class of maximum margin	▶ 00:49
learning algorithms,	▶ 00:51
and the 2 most popular are	▶ 00:53
support vector machines and boosting.	▶ 00:56
If you are familiar with machine learning,	▶ 01:00
you've come across these terms.	▶ 01:02
These are very frequently used these days	▶ 01:04
in actual discrimination learning tasks.	▶ 01:07
I will not go into any details because it would go	▶ 01:10
way beyond the scope of this introduction	▶ 01:12
to artificial intelligence class, but let's see	▶ 01:16
a few abstract words specifically about	▶ 01:18
support vector machines or SVMs.	▶ 01:21
As I said before a support vector machine	▶ 01:25
derives a linear separator, and it takes	▶ 01:30
the one that actually maximizes the margin	▶ 01:34
as shown over here.	▶ 01:39
By doing so it attains additional robost-ness	▶ 01:42
over perceptron which only picks	▶ 01:44
a linear separator without	▶ 01:46
consideration of the margin.	▶ 01:48
Now the problem of finding the	▶ 01:51
margin maximizing linear separator	▶ 01:53
can be solved by a quadratic program	▶ 01:55
which is an integer method for finding the best	▶ 01:59
linear separator that maximizes the margin.	▶ 02:03
One of the nice things that support	▶ 02:06
vector machines do in practice is	▶ 02:08
they use linear techniques to solve	▶ 02:12
nonlinear separation problems,	▶ 02:16
and I'm just going to give you a glimpse of	▶ 02:19
what's happening without going into any detail.	▶ 02:22
Suppose the data looks as follows:	▶ 02:25
we have a positive class	▶ 02:28
which is near the origin of a coordinate system	▶ 02:31
and a negative class that surrounds the positive class.	▶ 02:33
Clearly these 2 classes	▶ 02:37
are not linearly separable	▶ 02:39
because there's no line I can draw that	▶ 02:41
separates the negative examples from the positive examples.	▶ 02:43
An idea that underlies SVMs,	▶ 02:47
that will ultimately be known as	▶ 02:49
the kernel trick,	▶ 02:51
is to augment the feature set by new features.	▶ 02:53
Suppose this is X1, and this is X2,	▶ 02:56
and normally X1 and X2	▶ 02:58
will be the input features.	▶ 03:00
In this example, you might derive	▶ 03:03
a 3rd one.	▶ 03:05
Let me pick a 3rd one	▶ 03:07
Suppose X3 equals the square root of	▶ 03:09
X1 square + X2 square.	▶ 03:13
In other words X3 is the distance	▶ 03:18
of any data point from the center	▶ 03:22
of the coordinate system.	▶ 03:25
Then things do become linearly separable	▶ 03:27
so that just along the 3rd dimension	▶ 03:31
all the positive examples end up	▶ 03:33
to be close to the origin,	▶ 03:36
and all the negative examples	▶ 03:39
are further away, and the line is	▶ 03:41
orthogonal to the 3rd input feature	▶ 03:43
solves the separation problem.	▶ 03:46
Map back into the space over here	▶ 03:49
is actually a circle which is a set of all	▶ 03:52
values of X3 that are equidistant	▶ 03:55
to the center of the origin.	▶ 04:00
Now this trick could be done in any linear learning algorithm,	▶ 04:02
and it's really an amazing trick.	▶ 04:06
You can take any nonlinear problem, add	▶ 04:08
features of this type or any other type,	▶ 04:10
and use linear techniques	▶ 04:13
and get better solutions.	▶ 04:15
This is a very deep machine learning insight	▶ 04:17
that you can extend your feature space	▶ 04:19
in this way, and there's numerous	▶ 04:21
papers written about this.	▶ 04:23
In SVMs, the extension of the feature space is mathematically done by	▶ 04:25
what's called a kernel.	▶ 04:31
I can't really tell you about this in this class,	▶ 04:33
but it makes it possible to write	▶ 04:36
very large new feature spaces including	▶ 04:38
infinitely dimensional new feature spaces.	▶ 04:41
These messages are very powerful.	▶ 04:44
It turns out you never	▶ 04:46
really compute all those features.	▶ 04:48
They are implicitly represented by	▶ 04:50
so called kernels, and if you care about this,	▶ 04:52
I recommend you to dive	▶ 04:55
deeper into the literature	▶ 04:57
of support vector machines.	▶ 04:59
This is meant to just give you	▶ 05:01
an overview of the essence of	▶ 05:03
what support vector machines are all about.	▶ 05:05
So in summary,	▶ 05:08
linear methods we learned about	▶ 05:10
using them for regression	▶ 05:12
and also classification.	▶ 05:15
We learned about exact solutions	▶ 05:17
versus iterative solutions.	▶ 05:19
We talked about smoothing,	▶ 05:23
and we even talked about	▶ 05:25
using linear methods for nonlinear problems.	▶ 05:27
So we covered quite a bit of ground.	▶ 05:30
This is a really significant cross section	▶ 05:33
of machine learning.	▶ 05:35

(02:01) 50 k Nearest Neighbors

Welcome to the online introduction to artificial intelligence.	▶ 00:00
My name is Sebastian Thrun. >>I'm Peter Norvig.	▶ 00:04
We are teaching this class at Stanford,	▶ 00:07
and now we are teaching it online for the entire world.	▶ 00:09
We are really excited about this.	▶ 00:11
It's great to have you all here.	▶ 00:13
It's exciting to have such a record-breaking number of people.	▶ 00:14
We think we can deliver a good introduction to artificial intelligence.	▶ 00:18
We hope you'll stick with it.	▶ 00:22
It's going to be a lot of work,	▶ 00:24
but we think it's going to be very rewarding.	▶ 00:25
The way that it is going to be organized is that	▶ 00:27
every week there is going to be new videos and with these videos, quizes.	▶ 00:29
With these quizzes, you can test your knowledge about AI.	▶ 00:32
We also post for the advanced version of this class, homework assignments and exams	▶ 00:35
on which you'll be quizzed.	▶ 00:38
We're going to grade those to give you a final score to see	▶ 00:40
if you can actually master artificial intelligence the same way	▶ 00:44
any good student at Stanford would do it.	▶ 00:47
If you do that, then at the end of the class, we'll sign a letter of accomplishment,	▶ 00:49
and let you know that you've achieved this and what your rank in the class was.	▶ 00:54
So I hope you have fun. Watch us on videotape.	▶ 00:58
We will teach you AI.	▶ 01:02
Participate in the discussion forum.	▶ 01:04
Ask your questions, and help others answer questions.	▶ 01:06
I hope we have a fantastic time ahead of us in the next 10 weeks.	▶ 01:09
Welcome to the class. We'll see you online.	▶ 01:12

Welcome to the first unit of Online Introduction to Artificial Intelligence.	▶ 00:00
I will be teaching you the very, very basics today.	▶ 00:05
This is Unit 1 of Artificial Intelligence.	▶ 00:09
Welcome.	▶ 00:14
The purpose of this class is twofold:	▶ 00:16
Number 1, to teach you the very basics of artificial intelligence	▶ 00:20
so you'll be able to talk to people in the field	▶ 00:25
and understand the basic tools of the trade;	▶ 00:29
and also, very importantly, to excite you about the field.	▶ 00:32
I have been in the field of artificial intelligence for about 20 years,	▶ 00:37
and it's been truly rewarding.	▶ 00:42
So I want you to participate in the beauty and the excitement of AI	▶ 00:44
so you can become a professional who gets the same reward	▶ 00:48
and excitement out of this field as I do.	▶ 00:52
The basic structure of this class involves videos	▶ 00:55
in which Peter or I will teach you something new,	▶ 01:00
then also quizzes, which we will ask you about your ability to answer AI questions,	▶ 01:03
and finally, answer videos in which we tell you what the right answer would have been	▶ 01:11
for the quiz that you might have falsely or incorrectly answered before.	▶ 01:17
This will all be reiterated, and every so often you get a homework assignment,	▶ 01:22
also in the form of quizzes but without the answers.	▶ 01:28
And then we also have video exams.	▶ 01:34
If you check our website, there's requirements	▶ 01:37
on how you have to do assignments and exams.	▶ 01:39
Please go to ai-class.org in this class.	▶ 01:43
An AI program is called wetware, a formula, or an intelligent agent.	▶ 01:48
Pick the one that fits best.	▶ 01:58

[Thrun] The correct answer is intelligent agent.	▶ 00:00
Let's talk about intelligent agents.	▶ 00:04
Here is my intelligent agent,	▶ 00:07
and it gets to interact with an environment.	▶ 00:11
The agent can perceive the state of the environment	▶ 00:17
through its sensors,	▶ 00:22
and it can affect its state through its actuators.	▶ 00:25
The big question of artificial intelligence is the function that maps sensors to actuators.	▶ 00:29
That is called the control policy for the agent.	▶ 00:37
So all of this class will deal with how does an agent make decisions	▶ 00:41
that it can carry out with its actuators based on past sensor data.	▶ 00:48
Those decisions take place many, many times,	▶ 00:54
and the loop of environment feedback to sensors, agent decision,	▶ 00:58
actuator interaction with the environment and so on is called perception action cycle.	▶ 01:03
So here is my very first quiz for you.	▶ 01:12
Artificial intelligence, AI, has successfully been used in finance,	▶ 01:15
robotics, games, medicine, and the Web.	▶ 01:21
Check any or all of those that apply.	▶ 01:26
And if none of them applies, check the box down here that says none of them.	▶ 01:28

So, checkers is an interesting game.	▶ 00:00
Here's the typical board of the game of checkers.	▶ 00:04
Your pieces might look like this,	▶ 00:08
and your opponent's pieces might look like this.	▶ 00:11
And apart from some very cryptic rules in checkers,	▶ 00:16
which I won't really discuss here, the board basically tells you	▶ 00:19
everything there is to know about checkers, so it's clearly fully observable.	▶ 00:23
It is deterministic because your move and your opponent's move	▶ 00:28
very clearly affect the state of the board in ways that have	▶ 00:33
absolutely no stochasticity.	▶ 00:36
It is also discrete because there's finitely many action choices	▶ 00:39
and finitely many board positions,	▶ 00:45
and obviously, it is adversarial, since your opponent is out to get you.	▶ 00:47

[Male narrator] The game of poker--is this partially observable, stochastic,	▶ 00:00
continuous, or adversarial?	▶ 00:06
Please check any or all of those that apply.	▶ 00:09

[Male narrator] I would argue poker is partially observable	▶ 00:00
because it can't be seen what is in your opponent's hands.	▶ 00:03
It is stochastic because you're being dealt cards that are kind of coming at random.	▶ 00:08
It is not continuous; it's just finding many cards	▶ 00:13
and finding many actions you can do, even though you might argue	▶ 00:16
that there's a huge number of different monies you can bet.	▶ 00:20
It's still finite, and it is clearly adversarial.	▶ 00:24
If you've ever played poker before, you know how brutal it can be.	▶ 00:27

[Male narrator] --a favorite, a robotic car.	▶ 00:00
I wish to know whether it is partially observable,	▶ 00:04
stochastic, continuous, or adversarial.	▶ 00:06
That is, is the problem of driving robotically--	▶ 00:11
say, in a city--subject to any of those 4 categories?	▶ 00:16
Please check any or all that might apply.	▶ 00:20

Well, the robotic car clearly deals with a partially observable environment	▶ 00:00
if you just look at momentary sensing input, you can't even tell how fast other cars are going.	▶ 00:04
So, you need to memorize something.	▶ 00:10
It is stochastic because it's inherently unpredictable	▶ 00:12
what's going to happen next with other cars.	▶ 00:15
It is continuous.	▶ 00:17
There's the infinitely many ways to set your steering	▶ 00:20
or push your gas pedal or your brake,	▶ 00:23
and, well, you can argue with adversarial or not.	▶ 00:26
Depending on where you live, it might be highly adversarial.	▶ 00:29
Where I live, it isn't.	▶ 00:31

I'm going to briefly talk of AI as something else,	▶ 00:00
which is AI is the technique of uncertainty management in computer software.	▶ 00:03
Put differently, AI is the discipline that you apply when you want to know what to do	▶ 00:10
when you don't know what to do.	▶ 00:17
Now, there's many reasons why there might be uncertainty in a computer program.	▶ 00:22
There could be a sensor limit.	▶ 00:27
That is, your sensors are unable to tell me	▶ 00:29
what exactly is the case outside the AI system.	▶ 00:33
There could be adversaries who act in a way that makes it hard for you	▶ 00:37
to understand what is the case.	▶ 00:41
There could be stochastic environments.	▶ 00:44
Every time you roll the dice in a dice game,	▶ 00:48
the stochasticity of the dice will make it impossible for you	▶ 00:51
to be absolutely certain of what's the situation.	▶ 00:55
There could be laziness.	▶ 00:57
So perhaps you can actually compute what the situation is,	▶ 01:00
but your computer program is just too lazy to do it.	▶ 01:04
And here's my favorite: ignorance, plain ignorance.	▶ 01:07
Many people are just ignorant of what's going on.	▶ 01:11
They could know it, but they just don't care.	▶ 01:14
All of these things are cause for uncertainty.	▶ 01:17
AI is the discipline that deals with uncertainty and manages it in decision making.	▶ 01:21

The answer is that chicken appears here,	▶ 00:01
here, here, and here.	▶ 00:04
Now, I don't know for sure, 100%, that that is the character for chicken in Chinese,	▶ 00:10
but I do know that there is a good correspondence.	▶ 00:14
Every place the word chicken appears in English,	▶ 00:17
this character appears in Chinese and no other place.	▶ 00:20
Let's go 1 step farther.	▶ 00:24
Let's see if we can work out a phrase in Chinese	▶ 00:27
and see if it corresponds to a phrase in English.	▶ 00:30
Here's the phrase corn cream.	▶ 00:33
Click on the characters in Chinese that correspond to corn cream.	▶ 00:38

The answer is: these 2 characters here	▶ 00:00
appear only in these 2 locations	▶ 00:04
corresponding to the words corn cream	▶ 00:07
which appear only in these locations in the English text.	▶ 00:10
Again, we're not 100% sure that's the right answer,	▶ 00:13
but it looks like a strong correlation.	▶ 00:17
Now, 1 more question.	▶ 00:20
Tell me what character or characters in Chinese	▶ 00:22
correspond to the English word soup.	▶ 00:26

The answer is that soup occurs in most of these phrases	▶ 00:00
but not 100% of them.	▶ 00:09
It's missing in this phrase.	▶ 00:11
Equivalently, on the Chinese side	▶ 00:14
we see this character occurs	▶ 00:17
in most of the phrases,	▶ 00:20
but it's missing here.	▶ 00:23
So we see that the correspondence doesn't have to be 100%	▶ 00:27
to tell us that there is still a good chance of a correlation.	▶ 00:31
When we're learning to do machine translation	▶ 00:34
we use these kinds of alignments to learn probability tables	▶ 00:37
of what is the probability of one phrase in one language	▶ 00:41
corresponding to the phrase in another language.	▶ 00:45

So congratulations, you just finished unit 1.	▶ 00:00
You just finished unit 1 of this class,	▶ 00:03
where I told you about key applications	▶ 00:07
of artificial intelligence,	▶ 00:10
I told you about the definition of an intelligent agent,	▶ 00:13
I gave you 4 key attributes of intelligent agents	▶ 00:18
(partial observability, stochasticity, continuous spaces, and adversarial natures),	▶ 00:24
I discussed sources and management of uncertainty,	▶ 00:31
and I briefly mentioned the mathematical concept of rationality.	▶ 00:34
Obviously, I only touched any of these issues superficially,	▶ 00:40
but as this class goes on you're going to dive into any of those	▶ 00:45
and learn much more about	▶ 00:49
what it takes to make a truly intelligent AI system.	▶ 00:51
Thank you.	▶ 00:55

[PROBLEM SOLVING]	▶ 00:00
In this unit we're going to talk about problem solving.	▶ 00:01
The theory and technology of building agents	▶ 00:04
that can plan ahead to solve problems.	▶ 00:06
In particular, we're talking about problem solving	▶ 00:10
where the complexity of the problem comes from the idea that there are many states.	▶ 00:13
As in this problem here.	▶ 00:17
A navigation problem where there are many choices to start with.	▶ 00:19
And the complexity comes from picking the right choice now and picking the right choice at the	▶ 00:24
next intersection and the intersection after that.	▶ 00:29
Streaming together a sequence of actions.	▶ 00:32
This is in contrast to the type of complexity shown in this picture,	▶ 00:35
where the complexity comes from the partial observability	▶ 00:39
that we can't see through the fog where the possible paths are.	▶ 00:43
We can't see the results of our actions	▶ 00:46
and even the actions themselves are not known.	▶ 00:48
This type of complexity will be covered in a later unit.	▶ 00:51
Here's an example of a problem.	▶ 00:56
This is a route-finding problem where we're given a start city,	▶ 00:58
in this case, Arad, and a destination, Bucharest, the capital of Romania,	▶ 01:03
from which this is a corner of the map.	▶ 01:09
And the problem then is to find a route from Arad to Bucharest.	▶ 01:11
The actions that the agent can execute when driving	▶ 01:16
from one city to the next along one of the roads shown on the map.	▶ 01:20
The question is, is there a solution that the agent can come up with	▶ 01:23
given the knowledge shown here to the problem of driving from Arad to Bucharest?	▶ 01:28

Now we see how to modify the Tree Search Function	▶ 00:00
to make it be a Graph Search Function	▶ 00:04
to avoid those repeated paths.	▶ 00:06
What we do, is we start off and initialize a set	▶ 00:09
called the explored set of states that we have already explored.	▶ 00:13
Then, when we consider a new path,	▶ 00:16
we add the new state to the set of already explored states,	▶ 00:19
and then when we are expanding the path	▶ 00:23
and adding in new states to the end of it,	▶ 00:26
we don’t add that in if we have already seen that new state	▶ 00:29
in either the frontier or the explored.	▶ 00:33
Now back to Breadth First Search.	▶ 00:37
Let’s assume we are using the Graph Search	▶ 00:39
so that we have eliminated the duplicate paths.	▶ 00:41
Arad is crossed off the list.	▶ 00:44
The path that goes from Arad to Sibiu	▶ 00:47
and back to Arad is removed,	▶ 00:49
and we are left with these one, two, three,	▶ 00:51
four, five possible paths.	▶ 00:53
Given these 5 paths,	▶ 00:57
show me which ones are candidates to be expanded next	▶ 00:59
by the Breadth First Search Algorithm.	▶ 01:02

[Male narrator] And the answer is that Breadth - First Search always considers	▶ 00:00
the shortest paths first, and in this case, there's 2 paths of length 1,	▶ 00:03
and 1, the paths from Arad to Zerind and Arad to Timisoara,	▶ 00:08
so those would be the 2 paths that would be considered.	▶ 00:12
Now, let's suppose that the tie is broken in some way	▶ 00:15
and we chose this path from Arad to Zerind.	▶ 00:18
Now, we want to expand that node.	▶ 00:22
We remove it from the frontier and put it in the explored list	▶ 00:25
and now we say, "What paths are we going to add?"	▶ 00:31
So check off the ends of the paths the cities that we're going to add.	▶ 00:35

[Male narrator] In this case, there's nothing to add	▶ 00:00
because of the 2 neighbors, 1 is in the explored list and 1 is in the frontier,	▶ 00:03
and if we're using graph search, then we won't add either of those.	▶ 00:09

[Male narrator] So we move on, we look for another shortest path.	▶ 00:00
There's one path left of length 1, so we look at that path, we expand it,	▶ 00:04
add in this path, put that one on the explored list,	▶ 00:11
and now we've got 3 paths of length 2.	▶ 00:16
We choose 1 of them, and let's say we choose this one.	▶ 00:20
Now, my question is show me which states we add to the path	▶ 00:23
and tell me whether we're going to terminate the algorithm at this point	▶ 00:30
because we've reached the goal or whether we're going to continue.	▶ 00:35

[Male narrator] The answer is that we add 1 more path, the path to Bucharest.	▶ 00:00
We don't add the path going back because it's in the explored list,	▶ 00:08
but we don't terminate it yet.	▶ 00:11
True, we have added a path that ends in Bucharest,	▶ 00:13
but the goal test isn't applied when we add a path to the frontier.	▶ 00:16
Rather, it's applied when we remove that path from the frontier,	▶ 00:22
and we haven't done that yet.	▶ 00:26

[Male narrator] Now, why doesn't the general tree search or graph search algorithm stop	▶ 00:00
when it adds a goal node to the frontier?	▶ 00:06
The reason is because it might not be the best path to the goal.	▶ 00:09
Now, here we found a path of length 2	▶ 00:13
and we added a path of length 3 that reached the goal.	▶ 00:16
The general graph search or tree search doesn't know	▶ 00:21
that there might be some other path that we could expand	▶ 00:24
that would have a distance of say, 2-1/2,	▶ 00:27
but there's an optimization that could be made.	▶ 00:30
If we know we're doing Breadth - First Search	▶ 00:33
and we know there's no possibility of a path of length 2-1/2.	▶ 00:35
Then we can change algorithm so that it checks states	▶ 00:40
as soon as they're added to the frontier	▶ 00:44
rather than waiting until they're expanded	▶ 00:46
and in that case, we can write a specific Breadth - First Search routine	▶ 00:49
that terminates early and gives us a result as soon as we add a goal state to the frontier.	▶ 00:53
Breadth - First Search will find this path	▶ 01:01
that ends up in Bucharest, and if we're looking for the shortest path	▶ 01:04
in terms of number of steps,	▶ 01:08
Breadth - First Search is guaranteed to find it,	▶ 01:10
But if we're looking for the shortest path in terms of total cost	▶ 01:12
by adding up the step costs, then it turns out	▶ 01:17
that this path is shorter than the path found by Breadth - First Search.	▶ 01:21
So let's look at how we could find that path.	▶ 01:26

(1) Unit 0w

(01:15) 1 Introduction

(15) Unit 1w

(02:00) 1 Introduction

(01:34) 2 Intelligent Agents

(06:28) 3 Applications of AI

(05:17) 4 Terminology

(00:52) 5 Checkers Answer

(00:12) 6 Poker

(00:30) 7 Poker Answer

(00:22) 8 Robotic Car

(00:33) 9 Robotic Car Answer

(01:28) 10 AI and Uncertainty

(04:00) 11 Examples of AI in Practice

(00:44) 12 Chinese Translation Answer

(00:29) 13 Chinese Translation Answer 2

(00:48) 14 Chinese Translation Answer 3

(00:56) 15 Congratulations

(42) Unit 2

(01:34) Topic 1, Introduction

(04:29) Topic 2, Route Finding Question

(02:55) Topic 3, Route Finding

(03:19) Topic 4, Tree Search

(02:54) Topic 5, Tree Search Answer

(01:05) Topic 6, Graph Search

(00:42) Topic 7, Graph Search Answer

(00:13) Topic 8, Graph Search Answer

(00:38) Topic 9, More Graph Search

(00:29) Topic 10, Graph Search Answer

(01:30) Topic 11, Graph Search Termination

(00:51) Topic 12, Uniform Cost Search

(00:44) Topic 13, Uniform Cost Search

(00:56) Topic 14, Uniform Cost Search

(00:15) Topic 15, Uniform Cost Search

(01:13) Topic 16, Uniform Cost Termination

(01:46) Topic 17, Uniform Cost Termination Answer

(01:50) Topic 18, Depth First Search

(01:49) Topic 19, Search Optimality Answer

(02:02) Topic 20, Storage Requirements, Completeness

(00:49) Topic 21, Completeness Answer

(04:28) Topic 22, More on Uniform Cost Search

(03:14) Topic 23, A-Star Search

(00:14) Topic 23, A-Star Search ANSWER

(00:39) Topic 24, A-Star Second Question

(00:12) Topic 24, A-Star Second Question ANSWER

(00:20) Topic 25, A-Star Third Question

(00:11) Topic 25, A-Star Third Question ANSWER

(00:26) Topic 26, A-Star Fourth Question

(00:23) Topic 26, A-Star Fourth Question ANSWER

(01:24) Topic 27, A-Star Fifth Question

(00:49) Topic 27, A-Star Fifth Question ANSWER Mandatory

(01:22) Topic 28, Optimistic Heuristics

(00:59) Topic 29, State Spaces

(00:35) Topic 29, State Spaces ANSWER

(01:44) Topic 30, State Space Diagram and More Complexity

(00:57) Topic 30, State Space Diagram and More Complexity ANSWER

(01:49) Topic 31, Sliding Blocks Puzzle

(00:42) Topic 31, Sliding Blocks Puzzle ANSWER

(03:16) Topic 32, Where is the Intelligence

(01:52) Topic 33, What Can't Search Do

(02:35) Topic 34, Note on Implementation

(16) Homework 1

(00:05) Congratulations!

(00:05) Introduction

(01:00) Question 1, Peg Solitaire

(00:22) Question 1, Peg Solitaire ANSWER

(00:54) Question 2, Loaded Coin

(00:38) Question 2, Loaded Coin ANSWER

(00:32) Question 3, Path Through Maze

(00:18) Question 3, Path Through Maze ANSWER

(00:43) Question 4, Search Tree

(00:38) Question 4, Search Tree ANSWER

(00:31) Question 5, Another Search Tree

(00:48) Question 5, Another Search Tree ANSWER

(01:00) Question 6, Search Network

(00:49) Question 6, Search Network ANSWER

(01:16) Question 7, A* Search

(01:44) Question 7, A* Search ANSWER

(64) Unit 3

(06:26) 1 Introduction

An algorithm that has traditionally been called uniform-cost search	▶ 00:00
but could be called cheapest-first search,	▶ 00:05
is guaranteed to find the path with the cheapest total cost.	▶ 00:08
Let's see how it works.	▶ 00:11
We start out as before in the start state.	▶ 00:14
And we pop that empty path off.	▶ 00:19
Move it from the frontier to explored,	▶ 00:24
and then add in the paths out of that state.	▶ 00:28
As before, there will be 3 of those paths.	▶ 00:33
And now, which path are we going to pick next	▶ 00:39
in order to expand according to the rules of cheapest first?	▶ 00:43

Cheapest first says that we pick the path with	▶ 00:00
the lowest total cost.	▶ 00:04
And that would be this path.	▶ 00:06
It has a cost of 75 compared to the cost of 118 and 140	▶ 00:07
for the other paths.	▶ 00:13
So we get here. We take that path off the frontier,	▶ 00:14
put it on the explored list, add in its neighbors.	▶ 00:19
Not going back to Arad,	▶ 00:23
but adding in this new path.	▶ 00:26
Summing up the total cost of that path,	▶ 00:30
71 + 75 is 146 for this path.	▶ 00:33
And now the question is,	▶ 00:40
which path gets expanded next?	▶ 00:41

Of the 3 paths on the frontier, we have ones	▶ 00:00
with a cost of 146, 140, and 118.	▶ 00:05
And that's the cheapest, so this one gets expanded.	▶ 00:10
We take it off the frontier, move it to explored,	▶ 00:13
add in its successors. In this case it's only 1.	▶ 00:16
And that has a path total of 229.	▶ 00:21
Which path do we expand next?	▶ 00:29
Well, we've got 146, 140, and 229	▶ 00:30
So 140 is the lowest.	▶ 00:33
Take it off the frontier. Put it on explored.	▶ 00:38
Add in this path	▶ 00:41
for a total cost of 220.	▶ 00:44
And this path for a total cost of 239.	▶ 00:48
And now the question is, which path do we expand next?	▶ 00:53

The answer is this one, 146.	▶ 00:00
Put it on explored.	▶ 00:04
But there's nothing to add because	▶ 00:07
both of its neighbors have already been explored.	▶ 00:12
Which path do we look at next?	▶ 00:13

The answer is this one. Two-twenty is less than 229 or 239.	▶ 00:00
Take it off the frontier. Put it on explored.	▶ 00:05
Add in 2 more paths and sum them up.	▶ 00:09
So, 220 plus 146 is 366.	▶ 00:15
And 220 plus 97 is 317.	▶ 00:21
Okay, and now, notice that we're closing in on Bucharest.	▶ 00:29
We've got 2 neighbors almost there, but neither of them is their turn yet.	▶ 00:32
Instead, the cheapest path is this one over here,	▶ 00:38
so move it to the explored list.	▶ 00:43
Add 70 to the path cost so far,	▶ 00:45
and we get 299.	▶ 00:50
Now the cheapest node is 239 here,	▶ 00:57
so we expand, finally, into Bucharest at a cost of 460.	▶ 01:01
And now the question is are we done? Can we terminate the algorithm?	▶ 01:09

[Male] And the answer is no, we're not done yet.	▶ 00:00
We've put Bucharest, the gold state, onto the frontier,	▶ 00:03
but we haven't popped it off the frontier yet.	▶ 00:07
And the reason is because we've got to look around and see if there's a better path	▶ 00:09
that can reach it, Bucharest.	▶ 00:13
And so, let's continue.	▶ 00:15
Look at everything on the frontier.	▶ 00:18
Here's the cheapest one over here.	▶ 00:20
Expand that.	▶ 00:23
Now, what's the cheapest next one?	▶ 00:26
Well, over here.	▶ 00:30
Oops, forgot to take this one off the list.	▶ 00:33
So now, we're at 317 plus 101 gives us another path into Bucharest,	▶ 00:36
and this is a better path.	▶ 00:44
This is 418, gives us another route in.	▶ 00:46
But we have to keep going.	▶ 00:54
The best path on the frontier is 366,	▶ 00:59
so pop that off, and that would give us 2 more routes into here,	▶ 01:06
and eventually we pop off all of these.	▶ 01:14
And then we get to the point where 418 was the best path on the frontier.	▶ 01:18
We pop that off, and then we recognize that we'd reach the goal,	▶ 01:24
and the reason that uniform cost finds the optimal path, the cheapest cost,	▶ 01:29
is because it's guaranteed that it will first pop off this cheapest path,	▶ 01:35
the 418, before it gets to the more expensive path, like the 460.	▶ 01:40

So, we've looked at 2 search algorithms.	▶ 00:00
One, breadth-first search, in which we always expand first	▶ 00:03
the shallowest paths, the shortest paths.	▶ 00:08
Second, cheapest-first search, in which we always expand first the path	▶ 00:12
with the lowest total cost.	▶ 00:17
And I'm going to take this opportunity to introduce a third algorithm, depth-first search,	▶ 00:20
which is in a way the opposite of breadth-first search.	▶ 00:25
In depth-first search, we always expand first the longest path,	▶ 00:28
the path with the most lengths in it.	▶ 00:33
Now, what I want to ask you to do is for each of these nodes in each of the trees,	▶ 00:36
tell us in what order they're expanded,	▶ 00:42
first, second, third, fourth, fifth and so on by putting a number into the box.	▶ 00:44
And if there are ties, put that number in and resolve the ties in left to right order.	▶ 00:49
Then I want you to ask one more question or answer one more question	▶ 00:58
which is are these searches optimal?	▶ 01:03
That is, are they guaranteed to find the best solution?	▶ 01:06
And for breadth-first search, optimal would mean finding the shortest path.	▶ 01:11
If you think it's guaranteed to find the shortest path, check here.	▶ 01:16
For cheapest first, it would mean finding the path with the lowest total path cost.	▶ 01:21
Check here if you think it's guaranteed to do that.	▶ 01:26
And we'll allow the assumption that all costs have to be positive.	▶ 01:30
And in depth first, cheapest or optimal would mean, again,	▶ 01:34
as in breadth first, finding the shortest possible path in terms of number of lengths.	▶ 01:41
Check here if you think depth first will always find that.	▶ 01:46

Here are the answers.	▶ 00:00
Breadth-first search, as the name implies, expands nodes in this order.	▶ 00:04
One, 2, 3, 4, 5, 6, 7.	▶ 00:10
So, it's going across a stripe at a time, breadth first.	▶ 00:17
Is it optimal?	▶ 00:23
Well, it's always expanding in the shortest paths first,	▶ 00:25
and so wherever the goal is hiding, it's going to find it by examining	▶ 00:28
no longer paths, so in fact, it is optimal.	▶ 00:34
Cheapest first, first we expand the path of length zero,	▶ 00:38
then the path of length 2.	▶ 00:45
Now there's a path of length 4, path of length 5,	▶ 00:47
path of length 6, a path of length 7, and finally, a path of length 8.	▶ 00:53
And as we've seen, it's guaranteed to find the cheapest path of all,	▶ 01:02
assuming that all the individual step costs are not negative.	▶ 01:08
Depth-first search tries to go as deep as it can first,	▶ 01:14
so it goes 1, 2, 3, then backs up, 4,	▶ 01:17
then backs up, 5, 6, 7.	▶ 01:24
And you can see that it doesn't necessarily find the shortest path of all.	▶ 01:29
Let's say that there were goals in position 5 and in position 3.	▶ 01:34
It would find the longer path to position 3 and find the goal there	▶ 01:39
and would not find the goal in position 5.	▶ 01:43
So, it is not optimal.	▶ 01:46

Given the non-optimality of depth-first search,	▶ 00:00
why would anybody choose to use it?	▶ 00:04
Well, the answer has to do with the storage requirements.	▶ 00:07
Here I've illustrated a state space	▶ 00:10
consisting of a very large or even infinite binary tree.	▶ 00:13
As we go to levels 1, 2, 3, down to level n,	▶ 00:18
the tree gets larger and larger.	▶ 00:22
Now, let's consider the frontier for each of these search algorithms.	▶ 00:24
For breadth-first search, we know a frontier looks like that,	▶ 00:29
and so when we get down to level n, we'll require a storage space of	▶ 00:35
2 to the n of pass in a breadth-first search.	▶ 00:40
For cheapest first, the frontier is going to be more complicated.	▶ 00:45
It's going to sort of work out this contour of cost,	▶ 00:49
but it's going to have a similar total number of nodes.	▶ 00:53
But for depth-first search, as we go down the tree, we start going down this branch,	▶ 00:57
and then we back up, but at any point, our frontier is only going to have n nodes	▶ 01:03
rather than 2 to the n nodes, so that's a substantial savings for depth-first search.	▶ 01:08
Now, of course, if we're also keeping track of the explored set,	▶ 01:14
then we don't get that much savings.	▶ 01:19
But without the explored set, depth-first search has a huge advantage	▶ 01:21
in terms of space saved.	▶ 01:25
One more property of the algorithms to consider	▶ 01:27
is the property of completeness, meaning if there is a goal somewhere,	▶ 01:30
will the algorithm find it?	▶ 01:35
So, let's move from very large trees to infinite trees,	▶ 01:37
and let's say that there's some goal hidden somewhere deep down in that tree.	▶ 01:41
And the question is, are each of these algorithms complete?	▶ 01:47
That is, are they guaranteed to find a path to the goal?	▶ 01:51
Mark off the check boxes for the algorithms that you believe are complete in this sense.	▶ 01:55

The answer is that breadth-first search is complete,	▶ 00:00
so even if the tree is infinite, if the goal is placed at any finite level,	▶ 00:04
eventually, we're going to march down and find that goal.	▶ 00:10
Same with cheapest first.	▶ 00:16
No matter where the goal is, if it has a finite cost,	▶ 00:18
eventually, we're going to go down and find it.	▶ 00:21
But not so for depth-first search.	▶ 00:25
If there's an infinite path, depth-first search will keep following that,	▶ 00:28
so it will keep going down and down and down along this path	▶ 00:33
and never get to the path that the goal consists of	▶ 00:37
and never get to the path on which the goal sits.	▶ 00:42
So, depth-first search is not complete.	▶ 00:46