WEBVTT Kind: captions; Language: en-US

NOTE
Created on 2024-02-07T20:56:18.4162344Z by ClassTranscribe

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All right.

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Good morning, everybody.

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Hope you're doing well.

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So.

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And so I'll jump into it.

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All right.

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So previously we learned about a lot of

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different individual models, logistic

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regression, Keenan and so on.

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We also learned about trees that are

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able to learn features and split the

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feature space into different chunks and

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then make decisions and those different

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parts of the feature space.

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And then in the last class we learned

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about the bias variance tradeoff, that

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you can have a very complex classifier

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that requires a lot of data to learn

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and that might have low bias that can

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fit the training data really well, but

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high variance that you might get

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different classifiers with different

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samples of data.

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Or you can have a low bias.

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Or you can have a high bias, low

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variance classifier, a short tree, or a

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linear model that might not be able to

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fit the training data perfectly, but

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we'll do similarly on the test data to

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the training data.

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And then the escape of that is using.

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So usually you have this tradeoff where

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you have to choose one or the other,

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but ensembles are able to escape that

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tradeoff by combining multiple

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classifiers to either reduce the

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variance of each or reduce the bias of

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them.

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So this is so we also we talked

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particularly about boosted, boosted

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trees and random forests, which are two

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of the most powerful and widely useful

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classifiers and regressors and machine

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learning.

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The other is what we're starting to get

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into.

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We're starting to work our way towards

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neural networks, which as you know is

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like the is the dominant approach right

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now in machine learning.

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But before we get there, I want to

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introduce one more individual model

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which is the support vector machine.

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Support vector machines or SVM.

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So usually you'll just see people call

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it SVM without writing out the full

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name.

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They are developed in 1990s by Vapnik

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and his colleagues AT&T Bell Labs and

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it was based on statistical learning

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theory, so that their learning theory

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was actually developed by Vapnik and

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independently by others as early as the

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40s or 50s.

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But that led to the SVM algorithm in

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the 90s.

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And SVMS for a while were the most

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popular machine learning algorithm,

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mainly because they have a really good

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justification in terms of

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generalization, theory, theory and they

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can be optimized.

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And so for a while, people felt like

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Anna's were kind of a dead end.

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That's artificial neural networks are a

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dead end because they're a black box.

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They're hard to understand, they're

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hard to optimize, and VMS were able to

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get like similar performance, but are

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much better understood.

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So SVMS are kind of worth knowing and

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their own right?

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But actually the main reason that I'm

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decided to teach about SVMS is because

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there's a lot of other concepts

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associated with SVMS that are widely

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applicable that are worth knowing.

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So one is the generalization properties

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that they try to, for example, achieve

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a big margin.

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I'll explain what that means and.

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And have a decision that relies on

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limited training data, which is called

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structural risk minimization.

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Another is you can incorporate the idea

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of kernels, which is that you can

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define how 2 examples are similar and

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then use that as a basis of training a

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model.

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And related to that.

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We can see how you can formulate the

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same problem in different ways.

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So for SVMS, you can formulate it in

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what's called the primal, which just

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means that for a linear model you're

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saying that the model is a of all the

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features.

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Or you can formulate it in the dual,

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which is that you say that the weights

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are actually a sum of all the training

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examples, a of all the training

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examples.

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And I think it's just kind of

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interesting that.

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You can show that for many linear

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models, we tend to think of them as

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like it's that the linear model

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corresponds to feature importance, and

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you're learning a value for each

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feature, which is true, but the optimal

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linear model can often be expressed as

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just a combination of the training

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examples directly a weighted

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combination of the training examples.

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So it gives an interesting perspective

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I think.

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And then finally there's an

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optimization method for SVMS that was

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proposed that is called sub gradient,

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subgradient method and.

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Particularly it's called the general

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method is called sarcastic gradient

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descent and this is how optimization is

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done for neural networks.

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So I wanted to introduce it in the case

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of the SVMS where it's a little bit

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simpler before I get into.

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Perceptrons and MLPS multilayer

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perceptrons.

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So there's so there's three parts of

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this lecture.

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First, I'm going to talk about linear

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SVMS.

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And then I'm going to talk about

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kernels and nonlinear SVMS.

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And then finally the SVM optimization

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and.

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I might not get to the third part

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today, we'll see, but I don't want to

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rush it too much.

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But even if not, this leads naturally

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into the next lecture, which would

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basically be SGD on perceptrons, so.

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Alright, so SVMS are kind of pose a

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different answer to what's the best

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linear classifier.

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As we discussed previously, if you have

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a set of linearly separated data, these

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Red X's and Green OS, then there's

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actually a bunch of different linear

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models that could separate the X's from

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the O's.

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So logistic regression has one way of

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choosing the best model, which is

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you're maximizing the expected log

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likelihood of the labels given the

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data.

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So for given some boundary, it implies

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some probability for each of the data

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points.

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The data points that are really far

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from the boundary have like a really

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high confidence, and if that's correct,

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it means they have a low loss, and

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labels that are on the wrong side of

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the boundary or close to the boundary

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have a higher loss.

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And so as a result of that objective,

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the logistic regression depends on all

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the training examples.

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Even examples that are very confidently

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correct will contribute a little bit to

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the loss of the optimization.

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On the other hand, SVM makes a very

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different kind of decision.

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So SVM the goal is to make all of the

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examples at least minimally confident.

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So you want all the examples to be at

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least some distance from the boundary.

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And then the decision is based on a

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minimum set of examples, so that even

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if you were to remove a lot of the

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examples that want to actually change

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the decision.

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So this is so there's a little bit of

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terminology that comes with SVMS that's

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worth being careful about.

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One is the margin.

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So the margin is just the distance from

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the boundary of an example.

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So in this case this is an SVM fit to

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these examples and this is like the

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minimum margin of any of the examples.

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But the margin is just the distance

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from this boundary in the correct

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direction.

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So if an ex were over here, it would

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have like a negative margin because it

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would be on the wrong side of the

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boundary and if X is really far in this

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direction.

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Then it has a high positive margin.

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And the margin is normalized by the

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weight length.

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This is the L2 length of the weight.

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Because if you were if the data is

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linearly separable and you arbitrarily

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if you just like increase W if you

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multiply it by 1000 then this then the

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score of each data point will just

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linearly increase with the length of W

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so you need to normalize it by W.

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So mathematically the margin is just.

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This is the linear model W transpose X

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of weights times X plus some bias term

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B.

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I just want to note that bias term like

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in this context is not the same as

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classifier bias like.

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Classifier bias means that you can't

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fit like some kinds of decision

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boundaries, but the bias term is just

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adding a constant to your prediction.

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So we have a linear model here.

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It gets multiplied by Y.

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So in other words, if this is positive

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then I made a correct decision.

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And if this is negative, then I made an

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incorrect decision.

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If Y is -, 1 for example, but the model

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predicts A2, then this will be -, 2 and

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that's that means that I'm like kind of

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confidently incorrect.

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OK.

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And then the second term is a support

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vector, so support vector machines that

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has it in the title.

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A support vector is an example that

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lies on the margin of 1, so on that

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minimum margin.

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So the points that lie within a margin

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of one are the support vectors, and

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actually the decision only depends on

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those support vectors at the end.

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So the objective of the SVM is to try

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to minimize the sum of squared weights

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while preserving a margin of 1 S you

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could also cast it as that your weight

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vector is constrained to be unit

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length, but you want to maximize the

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margin.

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Those are just equivalent formulations.

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So here's so here's an example of an

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optimized model.

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Now here I added like a big probability

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mass of X's over here, and note that

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the SVM doesn't care about them at all.

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It only cares about these examples that

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are really close to this decision

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boundary between the O's and the ex's.

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So these three examples that are an

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equidistant from the decision boundary

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have they have like determined the

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decision boundary.

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These are the X's that are closest to

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the O's and the O that's closest to the

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X's, while these ones that are have a

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higher margin have not influenced the

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decision boundary.

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In fact, if you have a two, if the data

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is linearly separable and you have two

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two-dimensional features like I have

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here, these are the features X1 and X2,

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then there will always be 3 support

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vectors.

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Question.

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So yeah, good question.

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So the decision boundary is if the

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features are on one side of the

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boundary, then it's going to be one

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class, and if they're on the other side

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of the boundary then it will be the

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other class.

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And in terms of the linear model, if

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you have your model is W transpose X +

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B, so it's like a of the features plus

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the bias term.

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The decision boundary is where that

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value is 0.

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So if this value W transpose X + B.

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Is greater than zero, then you're

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predicting that the label is 1, and if

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this is less than zero, then you're

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predicting that the label is -, 1, and

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if it's equal to 0, then you're right

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on the boundary of that decision.

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There's a help.

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Yeah.

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If.

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And.

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So if the so the decision boundary

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actually it kind of it's not shown

00:13:10.320 --> 00:13:11.390
here, but it also kind of as a

00:13:11.390 --> 00:13:11.910
direction.

00:13:12.590 --> 00:13:14.958
So if things are on one side of the

00:13:14.958 --> 00:13:16.490
boundary then they would be X's, and if

00:13:16.490 --> 00:13:17.600
they're on the other side of the

00:13:17.600 --> 00:13:18.740
boundary then they'd be OS.

00:13:20.890 --> 00:13:22.920
And the boundary is fit to this data,

00:13:22.920 --> 00:13:25.775
so it's solved for in a way that this

00:13:25.775 --> 00:13:26.620
is true.

00:13:29.100 --> 00:13:30.080
Question.

00:13:30.080 --> 00:13:30.830
So how?

00:13:31.830 --> 00:13:35.420
Will perform when two data set are

00:13:35.420 --> 00:13:37.020
merged with each other, like when

00:13:37.020 --> 00:13:40.300
they're not separated, separable,

00:13:40.300 --> 00:13:41.310
mostly separable.

00:13:41.560 --> 00:13:43.020
They have a lot of emerging.

00:13:43.020 --> 00:13:44.902
Yeah, I'll get to that.

00:13:44.902 --> 00:13:45.220
Yeah.

00:13:45.220 --> 00:13:46.500
For now, I'm just dealing with this

00:13:46.500 --> 00:13:48.410
separable case where they can be

00:13:48.410 --> 00:13:49.906
perfectly classified.

00:13:49.906 --> 00:13:53.510
So the linear logistic regression

00:13:53.510 --> 00:13:55.379
behaves differently because it wants,

00:13:55.380 --> 00:13:57.240
these are a lot of data points and they

00:13:57.240 --> 00:14:00.760
will all have some loss even if they're

00:14:00.760 --> 00:14:02.314
like further away than other data

00:14:02.314 --> 00:14:03.372
points from the boundary.

00:14:03.372 --> 00:14:05.230
And so it wants them all to be really

00:14:05.230 --> 00:14:06.770
far from the boundary so that they're

00:14:06.770 --> 00:14:08.390
not incurring a lot of loss in total.

00:14:09.260 --> 00:14:11.633
So the linear logistic regression will

00:14:11.633 --> 00:14:13.880
push the line push the decision

00:14:13.880 --> 00:14:16.139
boundary away from this cluster of X's,

00:14:16.140 --> 00:14:17.970
even if it means that it has to be

00:14:17.970 --> 00:14:19.810
closer to one of the other ex's.

00:14:20.810 --> 00:14:22.650
And in some sense, this is a reasonable

00:14:22.650 --> 00:14:25.260
thing to do, because it makes your

00:14:25.260 --> 00:14:27.210
improves your overall average

00:14:27.210 --> 00:14:29.010
confidence in the correct label.

00:14:29.740 --> 00:14:32.143
Your average correct log confidence to

00:14:32.143 --> 00:14:33.310
be precise.

00:14:33.310 --> 00:14:37.100
But in another sense it's not so good

00:14:37.100 --> 00:14:38.640
because if you're if at the end of the

00:14:38.640 --> 00:14:39.930
day you're trying to minimize your

00:14:39.930 --> 00:14:42.230
classification error, they're very well

00:14:42.230 --> 00:14:44.380
could be other ex's that are in the

00:14:44.380 --> 00:14:46.570
test data that are around this point,

00:14:46.570 --> 00:14:47.940
and some of them might end up on the

00:14:47.940 --> 00:14:49.040
wrong side of the boundary.

00:14:56.200 --> 00:14:59.150
So this is the basic idea of the SVM,

00:14:59.150 --> 00:15:01.870
and the reason that SVMS are so popular

00:15:01.870 --> 00:15:04.380
is because they have really good

00:15:04.380 --> 00:15:05.590
marginalization.

00:15:05.590 --> 00:15:07.550
I mean really good generalization

00:15:07.550 --> 00:15:09.130
guarantees.

00:15:10.130 --> 00:15:14.360
So there's like 2 main Principles, 2

00:15:14.360 --> 00:15:16.720
main reasons that they generalize, and

00:15:16.720 --> 00:15:18.380
again generalize means that they will

00:15:18.380 --> 00:15:20.470
perform similarly to the test data

00:15:20.470 --> 00:15:21.700
compared to the training data.

00:15:24.090 --> 00:15:26.030
One is that maximizing the margin.

00:15:26.030 --> 00:15:28.320
So if all the examples are far from the

00:15:28.320 --> 00:15:30.570
margin, then you can be confident that

00:15:30.570 --> 00:15:31.770
other samples from the same

00:15:31.770 --> 00:15:33.896
distribution are probably also going to

00:15:33.896 --> 00:15:35.600
be correct on the correct side of the

00:15:35.600 --> 00:15:36.010
boundary.

00:15:38.430 --> 00:15:41.410
The second thing is that it doesn't

00:15:41.410 --> 00:15:43.380
depend on a lot of training samples.

00:15:44.630 --> 00:15:48.810
So even if most of these X's and O's

00:15:48.810 --> 00:15:50.640
disappeared, as long as these three

00:15:50.640 --> 00:15:52.150
examples were here, you would end up

00:15:52.150 --> 00:15:53.270
fitting the same boundary.

00:15:54.170 --> 00:15:56.630
And so for example one way that you can

00:15:56.630 --> 00:16:00.110
measure the that you can get an

00:16:00.110 --> 00:16:02.830
estimate of your test error is to do

00:16:02.830 --> 00:16:04.120
leave one out cross validation.

00:16:04.120 --> 00:16:06.310
Which is when you remove one data point

00:16:06.310 --> 00:16:08.570
from the training set and then train a

00:16:08.570 --> 00:16:10.715
model and then test it on that left out

00:16:10.715 --> 00:16:12.370
point and then you keep on changing

00:16:12.370 --> 00:16:13.350
which point is left out.

00:16:13.960 --> 00:16:15.290
If you do leave one out cross

00:16:15.290 --> 00:16:17.370
validation on this, then if you leave

00:16:17.370 --> 00:16:19.286
out any of these points that are not on

00:16:19.286 --> 00:16:20.690
the margin, that you're going to get

00:16:20.690 --> 00:16:23.710
them correct, because the boundary will

00:16:23.710 --> 00:16:25.440
be defined by only these three points

00:16:25.440 --> 00:16:26.050
anyway.

00:16:26.050 --> 00:16:27.779
In other words, leaving out any of

00:16:27.779 --> 00:16:29.130
these points not on the margin won't

00:16:29.130 --> 00:16:31.840
change the boundary, and so if they're

00:16:31.840 --> 00:16:33.140
correct in training, they'll also be

00:16:33.140 --> 00:16:34.040
corrected in testing.

00:16:35.290 --> 00:16:36.780
So that leads to this.

00:16:36.850 --> 00:16:38.905
On this there's a.

00:16:38.905 --> 00:16:42.170
There's a proof here of the expected

00:16:42.170 --> 00:16:43.000
test error.

00:16:43.690 --> 00:16:45.425
A bound on the expected test error.

00:16:45.425 --> 00:16:47.120
So the expected test error will be no

00:16:47.120 --> 00:16:49.430
more than the percent of training

00:16:49.430 --> 00:16:51.360
samples that are support vectors.

00:16:51.360 --> 00:16:53.440
So in this case it would be 3 divided

00:16:53.440 --> 00:16:55.110
by the total number of training points.

00:16:56.250 --> 00:17:00.253
Or if it's or, it could be also smaller

00:17:00.253 --> 00:17:00.486
than.

00:17:00.486 --> 00:17:02.140
It will be smaller than the smallest of

00:17:02.140 --> 00:17:02.760
these.

00:17:03.910 --> 00:17:08.460
The D squared is like the smallest, the

00:17:08.460 --> 00:17:11.040
diameter of the smallest ball that

00:17:11.040 --> 00:17:11.470
contains.

00:17:11.470 --> 00:17:13.161
It's a square of the diameter of the

00:17:13.161 --> 00:17:14.370
smallest ball that contains all these

00:17:14.370 --> 00:17:14.730
points.

00:17:15.420 --> 00:17:16.620
Compared to the margin.

00:17:16.620 --> 00:17:18.853
So in other words, if the data, if the

00:17:18.853 --> 00:17:20.695
margin is like pretty big compared to

00:17:20.695 --> 00:17:22.340
the general variance of the data

00:17:22.340 --> 00:17:24.580
points, then you're going to have a

00:17:24.580 --> 00:17:27.950
small test error and that proves a lot

00:17:27.950 --> 00:17:28.950
more complicated.

00:17:28.950 --> 00:17:30.930
So it's at the link though, yeah?

00:17:33.500 --> 00:17:36.120
We find that the support vector through

00:17:36.120 --> 00:17:38.430
operation, so I will get to the

00:17:38.430 --> 00:17:40.280
optimization too, yeah.

00:17:41.500 --> 00:17:42.160
Some.

00:17:42.160 --> 00:17:44.290
There's actually many ways to solve it,

00:17:44.290 --> 00:17:46.960
and in the third part I'll talk about.

00:17:47.960 --> 00:17:51.920
What is a stochastic gradient descent?

00:17:51.920 --> 00:17:56.200
Which is the most the fastest way and

00:17:56.200 --> 00:17:58.080
probably the preferred way right now,

00:17:58.080 --> 00:17:58.380
yeah?

00:18:14.040 --> 00:18:17.385
So you could say, I think that you

00:18:17.385 --> 00:18:18.880
could pose, I think you could

00:18:18.880 --> 00:18:19.810
equivalently.

00:18:20.470 --> 00:18:23.580
Pose the problem as you want to.

00:18:23.790 --> 00:18:24.410


00:18:25.410 --> 00:18:26.182
Maximum.

00:18:26.182 --> 00:18:31.216
So this distance here is like the is

00:18:31.216 --> 00:18:35.140
the West transpose X + b * Y.

00:18:35.140 --> 00:18:38.950
So in other words, if WTX is very far

00:18:38.950 --> 00:18:40.550
from the boundary then you have a high

00:18:40.550 --> 00:18:43.414
margin that's like the distance in this

00:18:43.414 --> 00:18:44.560
like plotted space.

00:18:45.920 --> 00:18:48.440
And if you just like arbitrarily

00:18:48.440 --> 00:18:50.380
increase W, then that distance is going

00:18:50.380 --> 00:18:52.030
to increase because you're multiplying

00:18:52.030 --> 00:18:54.050
X by a larger number for each of your

00:18:54.050 --> 00:18:54.460
weights.

00:18:55.160 --> 00:18:57.523
And so you need to kind of normal, you

00:18:57.523 --> 00:19:00.000
need to in some way normalize for the

00:19:00.000 --> 00:19:00.705
weight length.

00:19:00.705 --> 00:19:03.159
And one way to do that is to say you

00:19:03.160 --> 00:19:05.293
could say that I'm going to fix my

00:19:05.293 --> 00:19:07.740
weights to be unit length that they

00:19:07.740 --> 00:19:09.730
have to their weights can't just get

00:19:09.730 --> 00:19:10.824
like arbitrarily bigger.

00:19:10.824 --> 00:19:13.390
And I'm going to try to make the margin

00:19:13.390 --> 00:19:14.820
as big as possible given that.

00:19:15.790 --> 00:19:18.812
But I probably just first for.

00:19:18.812 --> 00:19:20.250
It's probably just an easier

00:19:20.250 --> 00:19:21.250
optimization problem.

00:19:21.250 --> 00:19:23.100
I'm not sure exactly why, but it's

00:19:23.100 --> 00:19:25.380
usually posed as you want to minimize

00:19:25.380 --> 00:19:26.550
the length of the weights.

00:19:27.250 --> 00:19:29.180
While maintaining that the margin is 1.

00:19:29.910 --> 00:19:32.079
And I think that it may be that this

00:19:32.080 --> 00:19:34.690
lends itself better to so.

00:19:34.690 --> 00:19:36.260
I haven't talked about it yet, but to

00:19:36.260 --> 00:19:38.120
when you have when the data is not

00:19:38.120 --> 00:19:40.295
linearly separable, then it's very easy

00:19:40.295 --> 00:19:42.677
to modify this objective to account for

00:19:42.677 --> 00:19:44.410
the data that can't be correctly

00:19:44.410 --> 00:19:44.980
classified.

00:19:47.520 --> 00:19:50.140
Did that follow that at all?

00:19:52.740 --> 00:19:53.160
OK.

00:19:56.510 --> 00:19:58.540
So.

00:20:00.950 --> 00:20:02.700
Alright, so in the separable case,

00:20:02.700 --> 00:20:04.360
meaning that you can perfectly classify

00:20:04.360 --> 00:20:05.700
your data with a linear model.

00:20:06.580 --> 00:20:08.630
The prediction is simply the sign of

00:20:08.630 --> 00:20:12.077
your linear model W transpose X + B so

00:20:12.077 --> 00:20:15.780
and the labels here are one and -, 1.

00:20:15.780 --> 00:20:17.295
You can see in like different cases,

00:20:17.295 --> 00:20:19.054
sometimes people say binary problem,

00:20:19.054 --> 00:20:21.045
the labels are zero or one and

00:20:21.045 --> 00:20:23.022
sometimes they'll say it's -, 1 or one.

00:20:23.022 --> 00:20:25.460
And it's mainly just chosen for the

00:20:25.460 --> 00:20:26.712
simplicity of the math.

00:20:26.712 --> 00:20:29.040
In this case it kind of makes it the

00:20:29.040 --> 00:20:29.350
make.

00:20:29.350 --> 00:20:31.080
It makes the math a lot simpler so I

00:20:31.080 --> 00:20:33.794
don't have to say like F y = 0 then

00:20:33.794 --> 00:20:36.030
this, if y = 1 then this other thing I

00:20:36.030 --> 00:20:36.410
can just.

00:20:36.490 --> 00:20:37.630
Y into the equation.

00:20:39.400 --> 00:20:42.540
The optimization is I'm going to solve

00:20:42.540 --> 00:20:45.960
for the West the weights that minimize

00:20:45.960 --> 00:20:48.930
that the smallest weights that satisfy

00:20:48.930 --> 00:20:49.850
this constraint.

00:20:50.680 --> 00:20:53.650
That the margin is one for all

00:20:53.650 --> 00:20:56.840
examples, so the model times the model

00:20:56.840 --> 00:20:59.826
prediction times the label is at least

00:20:59.826 --> 00:21:01.600
one for every training sample.

00:21:06.580 --> 00:21:09.440
If the data is not linearly separable,

00:21:09.440 --> 00:21:12.490
then I can just extend a little bit.

00:21:13.190 --> 00:21:14.520
And I can say.

00:21:15.780 --> 00:21:17.000
I don't know what that sound is.

00:21:17.000 --> 00:21:19.350
It's really weird, OK?

00:21:20.690 --> 00:21:23.210
And if the data is not linearly

00:21:23.210 --> 00:21:24.230
separable.

00:21:25.130 --> 00:21:26.900
Then I can say that I'm going to just

00:21:26.900 --> 00:21:30.240
pay a penalty of C times, like how much

00:21:30.240 --> 00:21:32.467
that data violates my margin.

00:21:32.467 --> 00:21:35.405
So the if it has a margin of less than

00:21:35.405 --> 00:21:39.533
one, then I pay C * 1 minus its margin.

00:21:39.533 --> 00:21:42.222
So for example if it's right on the

00:21:42.222 --> 00:21:44.280
boundary, then W transpose X + b is

00:21:44.280 --> 00:21:47.665
equal to 0 and so I pay a penalty of C

00:21:47.665 --> 00:21:49.380
* 1 if it's negative.

00:21:49.380 --> 00:21:50.638
If it's on the wrong side of the

00:21:50.638 --> 00:21:51.820
boundary, then I'd pay an even higher

00:21:51.820 --> 00:21:53.456
penalty, and if it's on the right side

00:21:53.456 --> 00:21:54.500
of the boundary, but.

00:21:54.560 --> 00:21:56.800
But the margin is less than one, then I

00:21:56.800 --> 00:21:57.810
pay a smaller penalty.

00:22:00.520 --> 00:22:03.040
This is called the hinge loss, and I'll

00:22:03.040 --> 00:22:04.050
show it here.

00:22:04.050 --> 00:22:06.210
So in the hinge loss, if you're

00:22:06.210 --> 00:22:08.130
confidently correct, there's zero

00:22:08.130 --> 00:22:10.110
penalty if you have a margin of greater

00:22:10.110 --> 00:22:12.080
than one in the case of an SVM.

00:22:12.750 --> 00:22:14.820
But if you're not confidently correct

00:22:14.820 --> 00:22:17.085
if they're unconfident or incorrect,

00:22:17.085 --> 00:22:18.980
which means which is when you're on

00:22:18.980 --> 00:22:20.640
this side of the decision boundary.

00:22:21.300 --> 00:22:24.460
Then you pay a penalty and the penalty

00:22:24.460 --> 00:22:26.070
just increases.

00:22:27.410 --> 00:22:30.220
Proportionally to how far you are from

00:22:30.220 --> 00:22:31.600
the margin of 1.

00:22:33.010 --> 00:22:35.640
And say if you have, if you're just

00:22:35.640 --> 00:22:37.350
unconfident way correct, you pay a

00:22:37.350 --> 00:22:38.803
little penalty, if you're incorrect,

00:22:38.803 --> 00:22:41.209
you pay a bigger penalty, and if you're

00:22:41.210 --> 00:22:42.760
confidently incorrect, then you pay an

00:22:42.760 --> 00:22:43.720
even bigger penalty.

00:22:45.420 --> 00:22:48.050
And this is important because.

00:22:48.780 --> 00:22:51.170
With this kind of loss, the confidently

00:22:51.170 --> 00:22:54.450
correct examples don't make any they

00:22:54.450 --> 00:22:56.090
don't change the decision.

00:22:56.090 --> 00:22:58.350
So anything that incurs a loss means

00:22:58.350 --> 00:23:00.000
that it's part of your thing that

00:23:00.000 --> 00:23:01.420
you're minimizing and your objective

00:23:01.420 --> 00:23:02.190
function.

00:23:02.190 --> 00:23:04.070
But if it doesn't incur a loss, then

00:23:04.070 --> 00:23:07.180
it's not changing your objective

00:23:07.180 --> 00:23:09.710
evaluation, so it's not causing any

00:23:09.710 --> 00:23:10.760
change to your decision.

00:23:15.700 --> 00:23:18.486
So I also need to note that there's

00:23:18.486 --> 00:23:20.373
like different ways of expressing the

00:23:20.373 --> 00:23:20.839
same thing.

00:23:20.839 --> 00:23:22.939
So here I express it in terms of this

00:23:22.940 --> 00:23:23.840
hinge loss.

00:23:23.840 --> 00:23:26.399
But you can also express it in terms of

00:23:26.400 --> 00:23:28.490
what people call slack variables.

00:23:28.490 --> 00:23:30.443
It's the exact same thing.

00:23:30.443 --> 00:23:32.850
It's just that here this slack variable

00:23:32.850 --> 00:23:35.220
is equal to 1 minus the margin.

00:23:35.220 --> 00:23:37.270
This is like if I bring.

00:23:39.220 --> 00:23:39.796
A.

00:23:39.796 --> 00:23:42.610
Bring this over here and then bring

00:23:42.610 --> 00:23:43.335
that over here.

00:23:43.335 --> 00:23:45.030
Then this slack variable when you

00:23:45.030 --> 00:23:47.030
minimize it will be equal to 1 minus

00:23:47.030 --> 00:23:47.730
this margin.

00:23:49.480 --> 00:23:51.330
So Slack variable is 1 minus the margin

00:23:51.330 --> 00:23:52.740
and you pay the same penalty.

00:23:52.740 --> 00:23:55.020
But if you're ever like reading about

00:23:55.020 --> 00:23:57.230
SVMS and somebody says like slack

00:23:57.230 --> 00:23:58.820
variable, then I just want you to know

00:23:58.820 --> 00:23:59.350
what that means.

00:24:00.260 --> 00:24:01.620
This means.

00:24:01.620 --> 00:24:03.760
So for this example here, we would be

00:24:03.760 --> 00:24:05.740
paying some penalty, some slack

00:24:05.740 --> 00:24:08.010
penalty, or some hinge loss penalty

00:24:08.010 --> 00:24:08.780
equivalently.

00:24:10.520 --> 00:24:12.840
Here's an example of an SVM decision

00:24:12.840 --> 00:24:15.510
boundary classifying between these red

00:24:15.510 --> 00:24:17.390
Oreos and Blue X's.

00:24:17.390 --> 00:24:19.270
This is from Andrews Esterman slides

00:24:19.270 --> 00:24:20.530
from Oxford.

00:24:22.710 --> 00:24:25.266
And here there's a soft margin, so

00:24:25.266 --> 00:24:27.210
there's some penalty.

00:24:27.210 --> 00:24:29.740
If you were to set this PC to Infinity,

00:24:29.740 --> 00:24:32.280
it means that you are still requiring

00:24:32.280 --> 00:24:34.820
that every example has a.

00:24:35.960 --> 00:24:37.930
Is has a margin of 1.

00:24:38.610 --> 00:24:40.310
Which that can be a problem if you have

00:24:40.310 --> 00:24:41.930
this case, because then you won't be

00:24:41.930 --> 00:24:43.360
able to optimize it because it's

00:24:43.360 --> 00:24:44.000
impossible.

00:24:45.030 --> 00:24:48.150
So if you set a small CC is 10, then

00:24:48.150 --> 00:24:49.790
you pay a small penalty when things

00:24:49.790 --> 00:24:50.495
violate the margin.

00:24:50.495 --> 00:24:52.360
And in this case it finds the decision

00:24:52.360 --> 00:24:54.180
boundary where it incorrectly

00:24:54.180 --> 00:24:57.270
classifies this one example and you

00:24:57.270 --> 00:25:00.473
have these four examples are within the

00:25:00.473 --> 00:25:00.829
margin.

00:25:00.830 --> 00:25:01.310
We're on it.

00:25:05.750 --> 00:25:06.300
OK.

00:25:06.300 --> 00:25:08.500
Any questions about that so far?

00:25:09.590 --> 00:25:09.980
OK.

00:25:11.890 --> 00:25:16.150
So I'm going to talk about the

00:25:16.150 --> 00:25:18.270
objective functions a little bit more,

00:25:18.270 --> 00:25:20.730
and to do that I'll introduce this

00:25:20.730 --> 00:25:22.180
thing called the Representer theorem.

00:25:22.940 --> 00:25:25.500
So the Representer theorem basically

00:25:25.500 --> 00:25:29.100
says that if you have some model, some

00:25:29.100 --> 00:25:31.240
linear model, that's W transpose X.

00:25:32.240 --> 00:25:37.240
Then the optimal West in many cases can

00:25:37.240 --> 00:25:43.100
be expressed as a of some weight for

00:25:43.100 --> 00:25:46.210
each example and the example features.

00:25:46.970 --> 00:25:49.240
And the label of the features or the

00:25:49.240 --> 00:25:50.450
label of the data point?

00:25:52.080 --> 00:25:55.300
So the optimal weight vector is just a

00:25:55.300 --> 00:25:58.160
weighted average of the input training

00:25:58.160 --> 00:25:59.270
example features.

00:26:02.260 --> 00:26:03.940
And there's certain like caveats and

00:26:03.940 --> 00:26:06.760
conditions, but this is true for L2

00:26:06.760 --> 00:26:10.550
logistic regression or SVM for example.

00:26:13.120 --> 00:26:17.500
And for SVMS these alphas are zeros for

00:26:17.500 --> 00:26:20.066
all the non support vectors because the

00:26:20.066 --> 00:26:22.080
support vectors influence the decision.

00:26:23.420 --> 00:26:24.800
So it's actually depends on a very

00:26:24.800 --> 00:26:26.390
small number of training examples.

00:26:28.710 --> 00:26:30.690
So I'm not going to go deep into the

00:26:30.690 --> 00:26:33.000
math and I don't expect anybody to be

00:26:33.000 --> 00:26:34.880
able to derive the dual or anything

00:26:34.880 --> 00:26:38.127
like that, but I just want to express

00:26:38.127 --> 00:26:39.940
express these objectives and different

00:26:39.940 --> 00:26:41.240
ways of looking at the problem.

00:26:42.100 --> 00:26:44.433
So in terms of prediction already I

00:26:44.433 --> 00:26:46.030
already gave you this formulation

00:26:46.030 --> 00:26:47.833
that's called the primal where the

00:26:47.833 --> 00:26:49.550
where you're optimizing in terms of the

00:26:49.550 --> 00:26:50.310
feature weights.

00:26:51.570 --> 00:26:53.550
And then you can also represent it in

00:26:53.550 --> 00:26:56.020
terms of you can represent, whoops, the

00:26:56.020 --> 00:26:56.700
dual.

00:26:57.700 --> 00:26:58.280
Where to go?

00:26:59.380 --> 00:27:01.400
Alright, you can also represent it in

00:27:01.400 --> 00:27:03.595
what's called a dual, where instead of

00:27:03.595 --> 00:27:05.330
optimizing over feature weights, you're

00:27:05.330 --> 00:27:06.920
optimizing over the weights of each

00:27:06.920 --> 00:27:07.520
example.

00:27:08.160 --> 00:27:10.470
Where again sum of those weights of the

00:27:10.470 --> 00:27:12.720
examples gives you your weight vector.

00:27:13.560 --> 00:27:16.256
And remember that this weights are the

00:27:16.256 --> 00:27:19.560
sum of alpha YX and when I plug that in

00:27:19.560 --> 00:27:22.410
here then I see in the dual that my

00:27:22.410 --> 00:27:25.914
prediction is the sum of alpha Y and

00:27:25.914 --> 00:27:28.395
the dot product of each training

00:27:28.395 --> 00:27:30.910
example with the example that I'm

00:27:30.910 --> 00:27:31.680
predicting for.

00:27:33.230 --> 00:27:33.980
So this.

00:27:33.980 --> 00:27:36.540
So here there's like a, it's a.

00:27:36.540 --> 00:27:39.255
It's an average of the similarities of

00:27:39.255 --> 00:27:43.550
the training examples with the features

00:27:43.550 --> 00:27:45.330
that I'm making a prediction for.

00:27:46.110 --> 00:27:47.730
Where the similarity is defined by A

00:27:47.730 --> 00:27:49.020
dot product in this case.

00:27:50.820 --> 00:27:53.831
Dot product is the sum of the elements

00:27:53.831 --> 00:27:56.571
squared or the I mean squared but the

00:27:56.571 --> 00:27:58.820
sum of the product of the elements.

00:28:01.790 --> 00:28:05.558
And this is just plugging it into the

00:28:05.558 --> 00:28:06.950
into the.

00:28:06.950 --> 00:28:08.750
If I plug everything in and then write

00:28:08.750 --> 00:28:10.350
the objective of the dual it comes out

00:28:10.350 --> 00:28:11.030
to this.

00:28:13.950 --> 00:28:17.410
For an SVM, alpha sparse, which means

00:28:17.410 --> 00:28:19.080
most of the values are zero.

00:28:19.080 --> 00:28:22.115
So the SVM only depends on these few

00:28:22.115 --> 00:28:25.920
examples, and so it's only nonzero for

00:28:25.920 --> 00:28:27.640
the support vectors, the examples that

00:28:27.640 --> 00:28:28.560
are within the margin.

00:28:35.550 --> 00:28:37.460
So the reason that the dual will be

00:28:37.460 --> 00:28:40.280
helpful is that it.

00:28:41.900 --> 00:28:45.020
Is that it allows us to deal with a

00:28:45.020 --> 00:28:45.930
nonlinear case.

00:28:45.930 --> 00:28:48.422
So in the top example, we might say a

00:28:48.422 --> 00:28:50.180
linear classifier is OK, it only gets

00:28:50.180 --> 00:28:51.550
one example wrong.

00:28:51.550 --> 00:28:53.179
I can live with that.

00:28:53.180 --> 00:28:55.437
But in the bottom case, a linear

00:28:55.437 --> 00:28:57.860
example seems like a really bad choice,

00:28:57.860 --> 00:28:58.210
right?

00:28:58.210 --> 00:29:00.995
Like it's obviously nonlinear and a

00:29:00.995 --> 00:29:02.010
linear classifier is going to get

00:29:02.010 --> 00:29:02.780
really high error.

00:29:03.530 --> 00:29:06.790
So what is some way that I could try

00:29:06.790 --> 00:29:08.630
to, let's say I still want to stick

00:29:08.630 --> 00:29:10.220
with a linear classifier, what's

00:29:10.220 --> 00:29:12.750
something that I can do to this do in

00:29:12.750 --> 00:29:16.375
this case to improve the ability of the

00:29:16.375 --> 00:29:16.950
linear classifier?

00:29:19.410 --> 00:29:19.860
Yeah.

00:29:22.680 --> 00:29:24.680
So I can like I can change the

00:29:24.680 --> 00:29:26.880
coordinate system or change the

00:29:26.880 --> 00:29:28.740
features in some way so that they

00:29:28.740 --> 00:29:30.140
become linearly separable.

00:29:30.930 --> 00:29:32.160
And the new feature space.

00:29:32.230 --> 00:29:34.440
Can we reject it in different

00:29:34.440 --> 00:29:34.890
dimensions?

00:29:37.530 --> 00:29:38.160
Right, yeah.

00:29:38.160 --> 00:29:40.230
And we can also project it into a

00:29:40.230 --> 00:29:41.810
higher dimensional space, for example,

00:29:41.810 --> 00:29:43.300
where it is linearly separable.

00:29:44.200 --> 00:29:45.666
Exactly those are the two.

00:29:45.666 --> 00:29:47.950
I think there's either 2 valid answers

00:29:47.950 --> 00:29:48.750
that I can think of.

00:29:49.900 --> 00:29:52.720
So for example, if we were to use polar

00:29:52.720 --> 00:29:56.040
coordinates, then we could represent

00:29:56.040 --> 00:29:59.273
instead of the like position on the X&Y

00:29:59.273 --> 00:30:01.190
axis or X1 and X2 axis.

00:30:01.910 --> 00:30:03.770
We could represent the distance and

00:30:03.770 --> 00:30:05.550
angle of each point from the center.

00:30:06.220 --> 00:30:08.980
And then here's that new coordinate

00:30:08.980 --> 00:30:09.350
space.

00:30:09.350 --> 00:30:11.300
And then this is a really easy like

00:30:11.300 --> 00:30:12.120
linear decision.

00:30:12.860 --> 00:30:14.440
So that's one way to solve it.

00:30:16.250 --> 00:30:18.520
Another way is that we can map the data

00:30:18.520 --> 00:30:21.520
into another higher dimensional space S

00:30:21.520 --> 00:30:23.550
if I instead represent instead of

00:30:23.550 --> 00:30:25.920
representing X1 and X2 directly.

00:30:25.920 --> 00:30:30.209
If I represent X1 squared and X2

00:30:30.209 --> 00:30:33.620
squared and the X1 times X2.

00:30:34.450 --> 00:30:35.960
Sqrt 2.

00:30:36.180 --> 00:30:38.580
Come it's helpful in the in some math

00:30:38.580 --> 00:30:39.240
later.

00:30:39.240 --> 00:30:41.830
If I represent these three coordinates

00:30:41.830 --> 00:30:44.680
instead, then it gets mapped as is

00:30:44.680 --> 00:30:47.545
shown in this 3D plot, and now there's

00:30:47.545 --> 00:30:51.020
a linear like a plane boundary that can

00:30:51.020 --> 00:30:54.040
separate the circles from the

00:30:54.040 --> 00:30:54.630
triangles.

00:30:55.490 --> 00:30:57.110
So this also works right?

00:30:57.110 --> 00:30:57.920
Two ways to do it.

00:30:57.920 --> 00:31:00.270
I can change the features or map into a

00:31:00.270 --> 00:31:01.380
higher dimensional space.

00:31:04.820 --> 00:31:07.180
So if I wanted to so I can write this

00:31:07.180 --> 00:31:09.740
as I have some kind of transformation

00:31:09.740 --> 00:31:12.190
on my input features and then given

00:31:12.190 --> 00:31:13.730
that transformation I then have a

00:31:13.730 --> 00:31:16.510
linear model and I can solve that using

00:31:16.510 --> 00:31:17.860
an SVM if I want.

00:31:24.020 --> 00:31:27.569
So if I'm representing this in the

00:31:27.570 --> 00:31:30.130
directly in the primal, then I can say

00:31:30.130 --> 00:31:33.090
that I just map my original features to

00:31:33.090 --> 00:31:34.970
my new features through this fee.

00:31:34.970 --> 00:31:37.120
Just some feature function.

00:31:37.980 --> 00:31:40.220
And then I solve for my weights in the

00:31:40.220 --> 00:31:41.030
new space.

00:31:42.030 --> 00:31:43.540
Sometimes though, in order to make the

00:31:43.540 --> 00:31:45.225
data linearly separable you might have

00:31:45.225 --> 00:31:47.050
to map into a very high dimensional

00:31:47.050 --> 00:31:47.480
space.

00:31:47.480 --> 00:31:50.390
So here like doing this trick where I

00:31:50.390 --> 00:31:53.370
look at the squares and then the

00:31:53.370 --> 00:31:55.390
product of the individual variables

00:31:55.390 --> 00:31:57.510
only went from 2 to 3 dimensions.

00:31:57.510 --> 00:31:59.162
But if I had started with 1000

00:31:59.162 --> 00:32:01.510
dimensions and I was like looking at

00:32:01.510 --> 00:32:03.666
all products of pairs of variables,

00:32:03.666 --> 00:32:05.292
this would become very high

00:32:05.292 --> 00:32:05.680
dimensional.

00:32:07.400 --> 00:32:08.880
So I might want to avoid that.

00:32:10.320 --> 00:32:12.050
So we can use the dual and I'm not

00:32:12.050 --> 00:32:13.690
going to step through the equations,

00:32:13.690 --> 00:32:16.199
but it's just showing that in the dual,

00:32:16.200 --> 00:32:18.750
since we're before you had a decision

00:32:18.750 --> 00:32:20.850
in terms of a dot product of original

00:32:20.850 --> 00:32:22.960
features, now it's a dot product of the

00:32:22.960 --> 00:32:24.060
transform features.

00:32:24.680 --> 00:32:26.280
So it's just the transformed features

00:32:26.280 --> 00:32:28.180
transpose times the other transform

00:32:28.180 --> 00:32:28.650
features.

00:32:32.240 --> 00:32:35.300
And sometimes we don't even need to

00:32:35.300 --> 00:32:37.300
compute the transformed features.

00:32:37.300 --> 00:32:38.970
All we really need at the end of the

00:32:38.970 --> 00:32:41.022
day is this kernel function.

00:32:41.022 --> 00:32:43.410
The kernel is a similarity function.

00:32:43.410 --> 00:32:45.192
It's a certain kind of similarity

00:32:45.192 --> 00:32:48.860
function that defines how similar to

00:32:48.860 --> 00:32:49.790
feature vectors are.

00:32:50.510 --> 00:32:52.710
So I could compute it explicitly.

00:32:53.920 --> 00:32:56.120
By transforming the features and taking

00:32:56.120 --> 00:32:57.740
their dot product and then I could

00:32:57.740 --> 00:32:59.560
store this kernel value for all my

00:32:59.560 --> 00:33:01.655
pairs of features in the training set,

00:33:01.655 --> 00:33:02.900
for example, and then do my

00:33:02.900 --> 00:33:03.680
optimization.

00:33:04.330 --> 00:33:05.980
I don't necessarily need to compute it

00:33:05.980 --> 00:33:08.142
every time, and sometimes I don't need

00:33:08.142 --> 00:33:09.740
to compute it as at all.

00:33:11.500 --> 00:33:12.930
An example where I don't need to

00:33:12.930 --> 00:33:15.150
compute it is in this case where I was

00:33:15.150 --> 00:33:17.230
looking at the square of the individual

00:33:17.230 --> 00:33:17.970
variables.

00:33:18.610 --> 00:33:20.750
And the product of pairs of variables.

00:33:22.140 --> 00:33:25.190
You can show that if you like, do this

00:33:25.190 --> 00:33:27.920
multiplication of these two different

00:33:27.920 --> 00:33:29.830
feature vectors X&Z.

00:33:31.090 --> 00:33:32.823
Then and you expand it.

00:33:32.823 --> 00:33:34.970
Then you can see that it actually ends

00:33:34.970 --> 00:33:39.575
up being that the product of this Phi

00:33:39.575 --> 00:33:42.410
of X times Phi of Z.

00:33:43.260 --> 00:33:46.422
Is equal to the square of the dot

00:33:46.422 --> 00:33:46.780
product.

00:33:46.780 --> 00:33:49.473
So you can get the same benefit just by

00:33:49.473 --> 00:33:50.837
squaring the dot product.

00:33:50.837 --> 00:33:53.150
And you can compute the similarity just

00:33:53.150 --> 00:33:55.440
by squaring the dot product instead of

00:33:55.440 --> 00:33:56.650
needing the map into the higher

00:33:56.650 --> 00:33:58.660
dimensional space and then taking the

00:33:58.660 --> 00:33:59.230
dot product.

00:34:00.400 --> 00:34:02.120
So if you had like a very high

00:34:02.120 --> 00:34:03.540
dimensional feature, this would save a

00:34:03.540 --> 00:34:04.230
lot of time.

00:34:04.230 --> 00:34:07.340
You wouldn't need to compute a million

00:34:07.340 --> 00:34:10.910
dimensional upper upper D feature.

00:34:13.930 --> 00:34:15.680
And yeah.

00:34:16.550 --> 00:34:18.310
So one thing to note though, is that

00:34:18.310 --> 00:34:19.840
because you're learning in terms of the

00:34:19.840 --> 00:34:22.950
distance of pairs of examples, the

00:34:22.950 --> 00:34:24.760
optimization tends to be pretty slow

00:34:24.760 --> 00:34:26.389
for kernel methods, at least in the

00:34:26.390 --> 00:34:27.730
traditional kernel methods.

00:34:28.440 --> 00:34:30.520
There's the algorithm that Austria is a

00:34:30.520 --> 00:34:32.710
lot faster for kernels, although I'm

00:34:32.710 --> 00:34:35.050
not going to go into depth for its

00:34:35.050 --> 00:34:35.900
kernelized version.

00:34:35.900 --> 00:34:36.140
Yep.

00:34:39.220 --> 00:34:40.700
Gives us a vector.

00:34:42.920 --> 00:34:45.130
X transpose times Z.

00:34:46.120 --> 00:34:49.250
Z This one that gives us a scalar

00:34:49.250 --> 00:34:51.760
because and Z are the same length,

00:34:51.760 --> 00:34:53.000
they're just two different feature

00:34:53.000 --> 00:34:53.570
vectors.

00:34:54.580 --> 00:34:57.028
And so they're both like say north by

00:34:57.028 --> 00:34:57.314
one.

00:34:57.314 --> 00:34:59.430
So then I have a one by North Times

00:34:59.430 --> 00:35:02.490
north by one gives me a 1 by 1.

00:35:04.390 --> 00:35:05.942
Yeah, so it's a dot product.

00:35:05.942 --> 00:35:08.740
So that dot product of two vectors

00:35:08.740 --> 00:35:10.490
gives you just a single value.

00:35:14.290 --> 00:35:16.340
So there's various kinds of kernels

00:35:16.340 --> 00:35:17.260
that people use.

00:35:17.260 --> 00:35:18.400
Polynomial.

00:35:19.430 --> 00:35:23.005
The one we talked about Gaussian, which

00:35:23.005 --> 00:35:25.610
is where you say that the similarity is

00:35:25.610 --> 00:35:28.630
based on how the squared distance

00:35:28.630 --> 00:35:30.060
between two feature vectors.

00:35:31.670 --> 00:35:32.360
And.

00:35:33.050 --> 00:35:34.730
And they can all just be used in the

00:35:34.730 --> 00:35:37.440
same way by computing the kernel value.

00:35:37.440 --> 00:35:39.100
In some cases you might compute

00:35:39.100 --> 00:35:40.700
explicitly, like for the Gaussian

00:35:40.700 --> 00:35:42.726
kernel and other places, and other

00:35:42.726 --> 00:35:44.550
cases there's a shortcut for the

00:35:44.550 --> 00:35:45.170
polynomial.

00:35:46.800 --> 00:35:49.010
But you just plug in your kernel

00:35:49.010 --> 00:35:50.190
function and then you can do this

00:35:50.190 --> 00:35:51.040
optimization.

00:35:52.850 --> 00:35:54.760
So I'm going to talk about optimization

00:35:54.760 --> 00:35:56.800
a little bit later, so I just want to

00:35:56.800 --> 00:35:58.430
show a couple of examples of how the

00:35:58.430 --> 00:36:00.410
decision boundary can be affected by

00:36:00.410 --> 00:36:02.090
some of the SVM parameters.

00:36:02.790 --> 00:36:05.910
So one of the parameters is CC is like.

00:36:05.910 --> 00:36:07.660
How important is it to make sure that

00:36:07.660 --> 00:36:10.625
every example is like outside the

00:36:10.625 --> 00:36:11.779
margin or on the margin?

00:36:12.530 --> 00:36:14.950
If it's Infinity, then you're forcing

00:36:14.950 --> 00:36:16.020
a, correct?

00:36:16.020 --> 00:36:18.630
You're forcing that everything has a

00:36:18.630 --> 00:36:20.030
margin of at least one.

00:36:20.810 --> 00:36:22.750
And so I wouldn't even be able to solve

00:36:22.750 --> 00:36:24.610
it if I were doing a linear classifier.

00:36:24.610 --> 00:36:27.556
But in this case it's a RBF classifier

00:36:27.556 --> 00:36:30.060
RBF kernel, which means that the

00:36:30.060 --> 00:36:31.110
distance is defined.

00:36:31.110 --> 00:36:32.920
The distance between examples is

00:36:32.920 --> 00:36:35.510
defined as like this squared distance

00:36:35.510 --> 00:36:37.160
divided by some Sigma.

00:36:38.040 --> 00:36:40.390
Sigma squared, so in this case I can

00:36:40.390 --> 00:36:41.990
linearly separate it with the RBF

00:36:41.990 --> 00:36:43.490
kernel and I get this function.

00:36:44.140 --> 00:36:48.490
If I reduce C then I start to get I get

00:36:48.490 --> 00:36:51.300
some an additional sample that is

00:36:51.300 --> 00:36:53.880
within the margin over here, but on

00:36:53.880 --> 00:36:55.885
average examples are further from the

00:36:55.885 --> 00:36:57.260
margin because I've relaxed my

00:36:57.260 --> 00:36:57.970
constraints.

00:36:57.970 --> 00:36:59.840
So sometimes you can get a better

00:36:59.840 --> 00:37:02.820
classifier by you don't always want to

00:37:02.820 --> 00:37:05.140
have C equal to Infinity or force that

00:37:05.140 --> 00:37:06.970
everything is outside the margin, even

00:37:06.970 --> 00:37:07.860
if it's possible.

00:37:09.610 --> 00:37:10.715
Often you have to optimize.

00:37:10.715 --> 00:37:12.700
You have to do like some kind of cross

00:37:12.700 --> 00:37:14.710
validation to choose C and that's one

00:37:14.710 --> 00:37:16.330
of the things that I always hated about

00:37:16.330 --> 00:37:18.571
SVMS because they can take a while to

00:37:18.571 --> 00:37:19.770
optimize and you have to do that

00:37:19.770 --> 00:37:20.130
search.

00:37:22.990 --> 00:37:27.090
So the if you relax, even more so now

00:37:27.090 --> 00:37:28.215
there's like a very weak penalty.

00:37:28.215 --> 00:37:29.860
So now you have lots of things within

00:37:29.860 --> 00:37:30.390
the margin.

00:37:32.280 --> 00:37:34.499
Then the other parameter, your kernel

00:37:34.500 --> 00:37:37.570
sometimes has parameters, so the RBF

00:37:37.570 --> 00:37:40.630
kernel is how sharp your distance

00:37:40.630 --> 00:37:41.690
function is.

00:37:41.690 --> 00:37:43.190
So if Sigma is.

00:37:43.470 --> 00:37:47.625
A Sigma is 1 then whatever, it's one.

00:37:47.625 --> 00:37:50.240
If Sigma Sigma goes closer to zero

00:37:50.240 --> 00:37:53.440
though, your RBF kernel becomes more a

00:37:53.440 --> 00:37:55.165
nearest neighbor classifier, because if

00:37:55.165 --> 00:37:56.739
Sigma is really close to 0.

00:37:57.700 --> 00:37:59.730
Then it means that an example that

00:37:59.730 --> 00:38:01.760
you're really close to.

00:38:01.760 --> 00:38:03.857
Only if you're super close to an

00:38:03.857 --> 00:38:06.459
example will it have a will it have a

00:38:06.460 --> 00:38:08.970
high similarity, and examples that are

00:38:08.970 --> 00:38:11.035
further away will have much lower

00:38:11.035 --> 00:38:11.540
similarity.

00:38:12.360 --> 00:38:14.080
So you can see that with Sigma equals

00:38:14.080 --> 00:38:16.010
one you just fit like these circular

00:38:16.010 --> 00:38:17.090
decision functions.

00:38:17.820 --> 00:38:19.770
As Sigma gets smaller, it starts to

00:38:19.770 --> 00:38:21.680
become like a little bit more wobbly.

00:38:22.440 --> 00:38:24.050
This is the this is the decision

00:38:24.050 --> 00:38:25.960
boundary, this solid line, in case

00:38:25.960 --> 00:38:27.630
that's not clear, with the green on one

00:38:27.630 --> 00:38:29.310
side and the yellow on the other side.

00:38:30.140 --> 00:38:32.459
And then as it gets smaller, then it

00:38:32.460 --> 00:38:33.800
starts to become like a nearest

00:38:33.800 --> 00:38:34.670
neighbor classifier.

00:38:34.670 --> 00:38:36.370
So almost everything is a support

00:38:36.370 --> 00:38:38.140
vector except for the very easiest

00:38:38.140 --> 00:38:40.429
points on the interior here and the

00:38:40.430 --> 00:38:41.110
decision boundary.

00:38:41.110 --> 00:38:43.050
You can start to become really

00:38:43.050 --> 00:38:45.935
arbitrarily complicated, just like just

00:38:45.935 --> 00:38:47.329
like a nearest neighbor.

00:38:48.570 --> 00:38:49.150
Question.

00:38:50.520 --> 00:38:51.895
What?

00:38:51.895 --> 00:38:54.120
So yeah, good question.

00:38:54.120 --> 00:38:55.320
So Sigma is in.

00:38:55.320 --> 00:38:57.750
It's from this equation here where I

00:38:57.750 --> 00:39:00.720
say that the similarity of two examples

00:39:00.720 --> 00:39:04.350
is their distance, their L2 distance

00:39:04.350 --> 00:39:06.140
squared divided by two Sigma.

00:39:06.980 --> 00:39:07.473
Squared.

00:39:07.473 --> 00:39:09.930
So if Sigma is really high, then it

00:39:09.930 --> 00:39:11.500
means that my similarity falls off

00:39:11.500 --> 00:39:14.490
slowly as two examples get further away

00:39:14.490 --> 00:39:15.620
in feature space.

00:39:15.620 --> 00:39:18.050
And if it's really small then the

00:39:18.050 --> 00:39:20.210
similarity drops off really quickly.

00:39:20.210 --> 00:39:22.120
So if it's like close to 0.

00:39:22.970 --> 00:39:25.380
Then the closest example will just be

00:39:25.380 --> 00:39:27.390
way, way way closer than any of the

00:39:27.390 --> 00:39:28.190
other examples.

00:39:29.690 --> 00:39:31.070
According to the similarity measure.

00:39:32.440 --> 00:39:32.980
Yeah.

00:39:33.240 --> 00:39:35.970
The previous example we are discussing

00:39:35.970 --> 00:39:37.700
projecting features to higher

00:39:37.700 --> 00:39:38.580
dimensions, right?

00:39:38.580 --> 00:39:41.730
Yeah, so how can we be sure this is the

00:39:41.730 --> 00:39:43.650
minimum dimension we required to

00:39:43.650 --> 00:39:44.380
classify that?

00:39:45.130 --> 00:39:46.810
Particular features are example space

00:39:46.810 --> 00:39:47.240
we have.

00:39:49.810 --> 00:39:50.980
Sorry, can you ask it again?

00:39:50.980 --> 00:39:52.100
I'm not sure if I got it.

00:39:52.590 --> 00:39:55.120
Understand something so we know that we

00:39:55.120 --> 00:39:56.250
need to project it in different

00:39:56.250 --> 00:39:58.610
dimensions to classify that properly.

00:39:58.610 --> 00:40:01.210
In the previous example like so we said

00:40:01.210 --> 00:40:02.100
we discussed right?

00:40:02.100 --> 00:40:04.486
So how can we very sure what is the

00:40:04.486 --> 00:40:05.850
minimum our minimum dimension?

00:40:05.850 --> 00:40:08.659
So the question is how do you know what

00:40:08.660 --> 00:40:10.700
kernel you should use or how high you

00:40:10.700 --> 00:40:12.400
should project the data right?

00:40:12.980 --> 00:40:15.750
Yeah, that that's a problem that you

00:40:15.750 --> 00:40:17.523
don't really know, so you have to try.

00:40:17.523 --> 00:40:19.350
You can try different things and then

00:40:19.350 --> 00:40:21.200
you use your validation set to choose

00:40:21.200 --> 00:40:21.950
the best model.

00:40:22.930 --> 00:40:26.350
But that's a downside of SVMS that

00:40:26.350 --> 00:40:29.960
since the optimization for big data set

00:40:29.960 --> 00:40:32.700
can be pretty slow if you're using a

00:40:32.700 --> 00:40:33.120
kernel.

00:40:33.790 --> 00:40:36.000
And so it can be very time consuming to

00:40:36.000 --> 00:40:37.410
try to search through all the different

00:40:37.410 --> 00:40:38.620
parameters and different types of

00:40:38.620 --> 00:40:39.700
kernels that you could use.

00:40:41.420 --> 00:40:44.310
There's another trick which you could

00:40:44.310 --> 00:40:46.230
do, which is like you take a random

00:40:46.230 --> 00:40:47.150
forest.

00:40:48.650 --> 00:40:51.300
And you take the leaf node that each

00:40:51.300 --> 00:40:53.632
data point falls into as a binary

00:40:53.632 --> 00:40:55.690
variable, so it'll be a sparse binary

00:40:55.690 --> 00:40:56.140
variable.

00:40:56.920 --> 00:40:58.230
And then you can apply your linear

00:40:58.230 --> 00:40:59.690
classifier to it.

00:40:59.690 --> 00:41:01.480
So then you're like mapping it into

00:41:01.480 --> 00:41:03.650
this high dimensional space that kind

00:41:03.650 --> 00:41:05.540
of takes into account the feature

00:41:05.540 --> 00:41:08.800
structure and where the data should be

00:41:08.800 --> 00:41:10.190
like pretty linearly separable.

00:41:16.350 --> 00:41:19.396
So in summary of the kernels for

00:41:19.396 --> 00:41:21.560
kernels you can learn the classifiers

00:41:21.560 --> 00:41:23.120
in high dimensional feature spaces

00:41:23.120 --> 00:41:24.705
without actually having to map them

00:41:24.705 --> 00:41:25.090
there.

00:41:25.090 --> 00:41:26.380
We did for the polynomial.

00:41:26.380 --> 00:41:28.898
The data can be linearly separable in

00:41:28.898 --> 00:41:30.229
the high dimensional space.

00:41:30.230 --> 00:41:31.796
Even if it weren't highly separable,

00:41:31.796 --> 00:41:34.029
wasn't wasn't there weren't actually

00:41:34.029 --> 00:41:36.150
separable in the original feature

00:41:36.150 --> 00:41:36.520
space.

00:41:37.530 --> 00:41:40.830
And you can use the kernel for an SVM,

00:41:40.830 --> 00:41:42.760
but the concept of kernels it's also

00:41:42.760 --> 00:41:44.620
used in other learning algorithms, so

00:41:44.620 --> 00:41:46.200
it's just like a general concept worth

00:41:46.200 --> 00:41:46.710
knowing.

00:41:48.530 --> 00:41:51.890
All right, so it's time for a stretch

00:41:51.890 --> 00:41:52.750
break.

00:41:53.910 --> 00:41:56.160
And you can think about this question

00:41:56.160 --> 00:41:58.130
if you were to remove a support vector

00:41:58.130 --> 00:41:59.600
from the training set with the decision

00:41:59.600 --> 00:42:00.560
boundary change.

00:42:01.200 --> 00:42:03.799
And then after 2 minutes I'll give the

00:42:03.800 --> 00:42:06.150
answer to that and then I'll give an

00:42:06.150 --> 00:42:08.360
application example and talk about the

00:42:08.360 --> 00:42:09.380
Pegasus algorithm.

00:44:27.710 --> 00:44:30.520
So what's the answer to this?

00:44:30.520 --> 00:44:32.510
If I were to remove one of these

00:44:32.510 --> 00:44:35.240
examples, here is my decision boundary.

00:44:35.240 --> 00:44:36.540
You're going to change or not?

00:44:38.300 --> 00:44:40.580
Yeah, it will change right?

00:44:40.580 --> 00:44:42.120
If I moved any of the other ones, it

00:44:42.120 --> 00:44:42.760
wouldn't change.

00:44:42.760 --> 00:44:43.979
But if I remove one of the support

00:44:43.980 --> 00:44:45.655
vectors it's going to change because my

00:44:45.655 --> 00:44:46.315
support is changing.

00:44:46.315 --> 00:44:49.144
So if I remove this for example, then I

00:44:49.144 --> 00:44:51.328
think the line would like tilt this way

00:44:51.328 --> 00:44:53.944
so that it would depend on that X and

00:44:53.944 --> 00:44:54.651
this X.

00:44:54.651 --> 00:44:58.186
And if I remove this O then I think it

00:44:58.186 --> 00:45:00.240
would shift down this way so that it

00:45:00.240 --> 00:45:02.020
depends on this O and these X's.

00:45:02.660 --> 00:45:04.970
Birds find some boundary where three of

00:45:04.970 --> 00:45:06.920
those points are equidistant, 2 on one

00:45:06.920 --> 00:45:07.840
side and 1 on the other.

00:45:12.630 --> 00:45:14.120
Alright, so I'm going to give you an

00:45:14.120 --> 00:45:15.920
example of how it's used, and you may

00:45:15.920 --> 00:45:17.862
notice that almost all the examples are

00:45:17.862 --> 00:45:19.570
computer vision, and that's because I

00:45:19.570 --> 00:45:21.431
know a lot of computer vision and so

00:45:21.431 --> 00:45:22.700
that's always what occurs to me.

00:45:24.630 --> 00:45:29.090
But this is an object detection case,

00:45:29.090 --> 00:45:29.760
so.

00:45:30.620 --> 00:45:33.770
The method here it's like called

00:45:33.770 --> 00:45:35.790
sliding window object detection which

00:45:35.790 --> 00:45:37.370
you can visualize it as like you have

00:45:37.370 --> 00:45:38.853
some image and you take a little window

00:45:38.853 --> 00:45:41.230
and you slide it across the image and

00:45:41.230 --> 00:45:43.250
you extract a patch at each position.

00:45:44.180 --> 00:45:45.990
And then you rescale the image and do

00:45:45.990 --> 00:45:46.550
it again.

00:45:46.550 --> 00:45:48.467
So you end up with like a whole.

00:45:48.467 --> 00:45:50.290
You turn the image into a whole bunch

00:45:50.290 --> 00:45:53.290
of different patches of the same size.

00:45:54.400 --> 00:45:56.830
After rescaling them, but that

00:45:56.830 --> 00:45:59.690
correspond to different different

00:45:59.690 --> 00:46:01.650
overlapping patches at different

00:46:01.650 --> 00:46:03.170
positions and scales in the original

00:46:03.170 --> 00:46:03.550
image.

00:46:04.270 --> 00:46:06.360
And then for each of those patches you

00:46:06.360 --> 00:46:08.840
have to classify it as being the object

00:46:08.840 --> 00:46:10.470
of interest or not, in this case of

00:46:10.470 --> 00:46:11.120
pedestrian.

00:46:12.070 --> 00:46:14.830
Where pedestrian just means person.

00:46:14.830 --> 00:46:16.970
These aren't actually necessarily

00:46:16.970 --> 00:46:18.480
pedestrians like this guy's not on the

00:46:18.480 --> 00:46:19.000
road, but.

00:46:19.960 --> 00:46:20.846
This person.

00:46:20.846 --> 00:46:24.290
So these are all examples of patches

00:46:24.290 --> 00:46:26.126
that you would want to classify as a

00:46:26.126 --> 00:46:26.464
person.

00:46:26.464 --> 00:46:28.490
So you can see it's kind of difficult

00:46:28.490 --> 00:46:30.190
because there could be lots of

00:46:30.190 --> 00:46:31.880
different backgrounds or other people

00:46:31.880 --> 00:46:34.030
in the way and you have to distinguish

00:46:34.030 --> 00:46:36.580
it from like a fire hydrant that's like

00:46:36.580 --> 00:46:37.953
pretty far away and looks kind of

00:46:37.953 --> 00:46:39.420
person like or a lamp post.

00:46:42.390 --> 00:46:45.400
This method is to like extract

00:46:45.400 --> 00:46:46.330
features.

00:46:46.330 --> 00:46:48.060
Basically you normalize the colors,

00:46:48.060 --> 00:46:49.730
compute gradients, compute the gradient

00:46:49.730 --> 00:46:50.340
orientation.

00:46:50.340 --> 00:46:51.550
I'll show you an illustration in the

00:46:51.550 --> 00:46:53.760
next slide and then you apply a linear

00:46:53.760 --> 00:46:54.290
SVM.

00:46:55.040 --> 00:46:56.450
And so for each of these windows you

00:46:56.450 --> 00:46:57.902
want to say it's a person or not a

00:46:57.902 --> 00:46:58.098
person.

00:46:58.098 --> 00:46:59.840
So you train on some training set of

00:46:59.840 --> 00:47:01.400
images where you have some people that

00:47:01.400 --> 00:47:02.100
are annotated.

00:47:02.770 --> 00:47:04.650
And then you test on some held out set.

00:47:06.300 --> 00:47:09.515
So this is the feature representation.

00:47:09.515 --> 00:47:11.920
It's basically like where are the edges

00:47:11.920 --> 00:47:14.170
and the image and the patch and how

00:47:14.170 --> 00:47:15.470
strong are they and what are their

00:47:15.470 --> 00:47:16.185
orientations.

00:47:16.185 --> 00:47:18.460
It's called a hog or histogram of

00:47:18.460 --> 00:47:20.460
gradients representation.

00:47:21.200 --> 00:47:23.930
And this paper is cited over 40,000

00:47:23.930 --> 00:47:24.610
times.

00:47:24.610 --> 00:47:26.670
It's mostly for the hog features, but

00:47:26.670 --> 00:47:28.790
it was also the most effective person

00:47:28.790 --> 00:47:29.840
detector for a while.

00:47:34.610 --> 00:47:38.876
So it it's very effective.

00:47:38.876 --> 00:47:42.730
So these plots are the X axis is the

00:47:42.730 --> 00:47:44.432
number of false positives per window.

00:47:44.432 --> 00:47:47.180
So it's a chance that you misclassify

00:47:47.180 --> 00:47:49.040
one of these windows as a person when

00:47:49.040 --> 00:47:50.117
it's not really a person.

00:47:50.117 --> 00:47:52.460
It's like a fire hydrant or random

00:47:52.460 --> 00:47:53.520
leaves or something else.

00:47:54.660 --> 00:47:58.600
X axis, Y axis is the miss rate, which

00:47:58.600 --> 00:48:01.480
is the number of true people that you

00:48:01.480 --> 00:48:02.440
fail to detect.

00:48:03.080 --> 00:48:05.160
So the fact that it's way down here

00:48:05.160 --> 00:48:07.560
basically means that it never makes any

00:48:07.560 --> 00:48:09.630
mistakes on this data set, so it can

00:48:09.630 --> 00:48:13.110
classify it gets 99.8% of the fines,

00:48:13.110 --> 00:48:16.730
99.8% of the people, and almost never

00:48:16.730 --> 00:48:17.860
has false positives.

00:48:18.900 --> 00:48:20.400
That was on this MIT database.

00:48:21.040 --> 00:48:23.154
Then there's another data set which was

00:48:23.154 --> 00:48:25.140
like more, which was harder.

00:48:25.140 --> 00:48:27.490
Those were the examples I showed of St.

00:48:27.490 --> 00:48:29.170
scenes and more crowded scenes.

00:48:29.860 --> 00:48:32.870
And they're the previous approaches had

00:48:32.870 --> 00:48:35.230
like pretty high false positive rates.

00:48:35.230 --> 00:48:38.340
So as a rule of thumb I would say

00:48:38.340 --> 00:48:43.090
there's typically about 10,000 windows

00:48:43.090 --> 00:48:43.780
per image.

00:48:44.480 --> 00:48:46.427
So if you have like a false positive

00:48:46.427 --> 00:48:48.755
rate of 10 to the -, 4, that means that

00:48:48.755 --> 00:48:50.555
you make one mistake on every single

00:48:50.555 --> 00:48:50.920
image.

00:48:50.920 --> 00:48:51.650
On average.

00:48:51.650 --> 00:48:53.400
You like think that there's one person

00:48:53.400 --> 00:48:55.080
where there isn't anybody on average

00:48:55.080 --> 00:48:55.985
once per image.

00:48:55.985 --> 00:48:57.410
So that's kind of a that's an

00:48:57.410 --> 00:48:58.490
unacceptable rate.

00:48:59.950 --> 00:49:02.723
But this method is able to get like 10

00:49:02.723 --> 00:49:06.380
to the -, 6 which is a pretty good rate

00:49:06.380 --> 00:49:09.230
and still find like 70% of the people.

00:49:10.030 --> 00:49:11.400
So these like.

00:49:12.320 --> 00:49:14.665
These curves that are clustered here

00:49:14.665 --> 00:49:17.020
are all different SVMS.

00:49:17.020 --> 00:49:20.970
Linear SVMS, they also do.

00:49:21.040 --> 00:49:21.800


00:49:22.760 --> 00:49:23.060
Weight.

00:49:23.060 --> 00:49:23.930
Linear.

00:49:23.930 --> 00:49:25.860
Yeah, so the black one here is a

00:49:25.860 --> 00:49:28.110
kernelized SVM, which performs very

00:49:28.110 --> 00:49:30.130
similarly, but takes a lot longer to

00:49:30.130 --> 00:49:32.340
train and do inference, so it wouldn't

00:49:32.340 --> 00:49:32.890
be referred.

00:49:33.880 --> 00:49:35.790
And then the other previous approaches

00:49:35.790 --> 00:49:36.870
are doing worse.

00:49:36.870 --> 00:49:38.500
They have like higher false positives

00:49:38.500 --> 00:49:39.960
rates for the same detection rate.

00:49:42.860 --> 00:49:44.470
So that was just that was just one

00:49:44.470 --> 00:49:46.832
example, but as I said like SVMS where

00:49:46.832 --> 00:49:49.080
the dominant I think the most commonly

00:49:49.080 --> 00:49:50.903
used, I wouldn't say dominant, but most

00:49:50.903 --> 00:49:53.510
commonly used classifier for several

00:49:53.510 --> 00:49:53.930
years.

00:49:56.330 --> 00:49:58.440
So SVMS are good broadly applicable

00:49:58.440 --> 00:49:58.782
classifier.

00:49:58.782 --> 00:50:00.780
They have a strong foundation in

00:50:00.780 --> 00:50:01.970
statistical learning theory.

00:50:01.970 --> 00:50:04.000
They work even if you have a lot of

00:50:04.000 --> 00:50:05.480
weak features.

00:50:05.480 --> 00:50:08.400
You do have to tune the parameters like

00:50:08.400 --> 00:50:10.470
C and that can be time consuming.

00:50:11.160 --> 00:50:13.390
And if you're using nonlinear SVM, then

00:50:13.390 --> 00:50:14.817
you have to decide what kernel function

00:50:14.817 --> 00:50:16.560
you're going to use, which may involve

00:50:16.560 --> 00:50:19.010
even more tuning in it, and it means

00:50:19.010 --> 00:50:20.150
that it's going to be a slow

00:50:20.150 --> 00:50:21.940
optimization and slower inference.

00:50:22.860 --> 00:50:24.680
The main negatives of SVM, the

00:50:24.680 --> 00:50:25.550
downsides.

00:50:25.550 --> 00:50:27.160
It doesn't have feature learning as

00:50:27.160 --> 00:50:29.580
part of the framework, where trees for

00:50:29.580 --> 00:50:30.750
example, you're kind of learning

00:50:30.750 --> 00:50:32.620
features and for neural Nets you are as

00:50:32.620 --> 00:50:32.930
well.

00:50:33.770 --> 00:50:38.430
And it also can took could be very slow

00:50:38.430 --> 00:50:39.010
to train.

00:50:40.290 --> 00:50:42.930
Until Pegasus, which is the next thing

00:50:42.930 --> 00:50:44.510
that I'm talking about, South, this was

00:50:44.510 --> 00:50:46.660
like a much faster and simpler way to

00:50:46.660 --> 00:50:47.790
train these algorithms.

00:50:49.220 --> 00:50:50.755
So I'm not going to talk about the bad

00:50:50.755 --> 00:50:53.270
ways or they're slow ways to optimize

00:50:53.270 --> 00:50:53.380
it.

00:50:54.360 --> 00:50:56.750
So this is so the next thing I'm going

00:50:56.750 --> 00:50:57.710
to talk about.

00:50:57.980 --> 00:51:01.350
Is called Pegasus which is how you can

00:51:01.350 --> 00:51:04.100
optimize the SVM and it stands for

00:51:04.100 --> 00:51:06.510
primal estimated subgradient solver for

00:51:06.510 --> 00:51:07.360
SVM, so.

00:51:09.020 --> 00:51:11.095
Primal because you're solving it in the

00:51:11.095 --> 00:51:12.660
primal formulation where you're

00:51:12.660 --> 00:51:14.540
minimizing the weights and the margin.

00:51:15.460 --> 00:51:16.840
Estimated because that's where you're.

00:51:17.900 --> 00:51:20.090
The subgradient is because you're going

00:51:20.090 --> 00:51:21.860
to you're going to make decisions based

00:51:21.860 --> 00:51:24.970
on a subsample of the training data.

00:51:24.970 --> 00:51:27.030
So you're trying to take a step in the

00:51:27.030 --> 00:51:29.000
right direction based on a few training

00:51:29.000 --> 00:51:31.710
examples to solver for SVM.

00:51:33.540 --> 00:51:36.790
I found out yesterday when I was look

00:51:36.790 --> 00:51:39.460
searching for the paper that Pegasus is

00:51:39.460 --> 00:51:42.260
also like an assisted suicide system in

00:51:42.260 --> 00:51:42.880
Switzerland.

00:51:42.880 --> 00:51:45.420
So it's kind of an unfortunate name,

00:51:45.420 --> 00:51:46.820
unfortunate acronym.

00:51:48.920 --> 00:51:49.520
And.

00:51:50.550 --> 00:51:54.150
So the so this is the SVM problem that

00:51:54.150 --> 00:51:56.160
we want to solve, minimize the weights

00:51:56.160 --> 00:52:00.000
and while also minimizing the hinge

00:52:00.000 --> 00:52:01.260
loss on all the samples.

00:52:02.510 --> 00:52:04.200
But we can reframe this.

00:52:04.200 --> 00:52:06.780
We can reframe it in terms of one

00:52:06.780 --> 00:52:07.110
example.

00:52:07.110 --> 00:52:09.297
So we could say, well, let's say we

00:52:09.297 --> 00:52:10.870
want to minimize the weights and we

00:52:10.870 --> 00:52:12.706
want to minimize the loss for one

00:52:12.706 --> 00:52:13.079
example.

00:52:14.410 --> 00:52:17.200
Then we can ask like how would I change

00:52:17.200 --> 00:52:19.630
the weights if that were my objective?

00:52:19.630 --> 00:52:21.897
And if you want to know how you can

00:52:21.897 --> 00:52:23.913
improve something, improve some

00:52:23.913 --> 00:52:25.230
objective with respect to some

00:52:25.230 --> 00:52:25.780
variable.

00:52:26.670 --> 00:52:27.890
Then what you do is you take the

00:52:27.890 --> 00:52:30.260
partial derivative of the objective

00:52:30.260 --> 00:52:33.330
with respect to the variable, and if

00:52:33.330 --> 00:52:35.285
you want the objective to go down, this

00:52:35.285 --> 00:52:36.440
is like a loss function.

00:52:36.440 --> 00:52:38.090
So we wanted to go down.

00:52:38.090 --> 00:52:40.763
So I want to find the derivative with

00:52:40.763 --> 00:52:42.670
respect to my variable, in this case

00:52:42.670 --> 00:52:45.680
the weights, and I want to take a small

00:52:45.680 --> 00:52:47.450
step in the negative direction of that

00:52:47.450 --> 00:52:49.262
gradient of that derivative.

00:52:49.262 --> 00:52:51.750
So that will make my objective just a

00:52:51.750 --> 00:52:52.400
little bit better.

00:52:52.400 --> 00:52:53.990
It'll make my loss a little bit lower.

00:52:56.470 --> 00:52:58.690
And if I compute the gradient of this

00:52:58.690 --> 00:53:01.440
objective with respect to West.

00:53:02.110 --> 00:53:06.610
So the gradient of West squared is just

00:53:06.610 --> 00:53:10.502
is just two WI mean and also the

00:53:10.502 --> 00:53:11.210
gradient of.

00:53:11.210 --> 00:53:13.360
Again vector math like.

00:53:13.360 --> 00:53:15.750
You might not be familiar with doing

00:53:15.750 --> 00:53:17.440
like gradients of vectors and stuff,

00:53:17.440 --> 00:53:19.350
but it often works out kind of

00:53:19.350 --> 00:53:20.800
analogous to the scalars.

00:53:20.800 --> 00:53:23.385
So the gradient of W transpose W is

00:53:23.385 --> 00:53:24.130
also W.

00:53:26.290 --> 00:53:29.515
This loss function is this margin which

00:53:29.515 --> 00:53:30.850
is just Y of.

00:53:30.850 --> 00:53:32.650
This is like a dot product W transpose

00:53:32.650 --> 00:53:33.000
X.

00:53:34.380 --> 00:53:36.690
So the gradient of this with respect to

00:53:36.690 --> 00:53:39.770
West is.

00:53:39.830 --> 00:53:41.590
Negative YX, right?

00:53:42.320 --> 00:53:45.880
And so my gradient if I've got this Max

00:53:45.880 --> 00:53:46.570
here as well.

00:53:46.570 --> 00:53:49.260
So that means that if I'm already like

00:53:49.260 --> 00:53:50.890
confidently correct, then I have no

00:53:50.890 --> 00:53:52.780
loss so my gradient is 0.

00:53:53.620 --> 00:53:55.800
If I'm not confidently correct, if I'm

00:53:55.800 --> 00:53:58.380
within the margin of 1 then I have this

00:53:58.380 --> 00:54:01.630
loss and the size of this.

00:54:03.400 --> 00:54:06.490
The size of the size of the gradient.

00:54:07.180 --> 00:54:11.210
Is just one, has a magnitude of 1 and

00:54:11.210 --> 00:54:13.750
the direction because my hinge loss has

00:54:13.750 --> 00:54:14.320
this.

00:54:15.400 --> 00:54:17.315
So the size do the hinge loss is just

00:54:17.315 --> 00:54:18.900
one because the hinge loss just has a

00:54:18.900 --> 00:54:20.250
gradient of 1, it's just a straight

00:54:20.250 --> 00:54:20.550
line.

00:54:21.620 --> 00:54:24.950
And then the of this is YX, right?

00:54:24.950 --> 00:54:28.825
The gradient of YW transpose X is YX

00:54:28.825 --> 00:54:31.797
and so I get this gradient here, which

00:54:31.797 --> 00:54:35.696
is it's a 0 if my margin is good enough

00:54:35.696 --> 00:54:36.963
and it's a one.

00:54:36.963 --> 00:54:40.300
This term is A1 if I'm under the

00:54:40.300 --> 00:54:40.630
margin.

00:54:41.520 --> 00:54:44.500
Times Y which is one or - 1 depending

00:54:44.500 --> 00:54:46.419
on the label, times X which is the

00:54:46.420 --> 00:54:47.060
feature vector.

00:54:47.900 --> 00:54:48.830
So in other words.

00:54:49.930 --> 00:54:52.720
If I'm not happy with my score right

00:54:52.720 --> 00:54:56.070
now and let's say let's say W transpose

00:54:56.070 --> 00:54:58.690
X is oh .5 and y = 1.

00:54:59.660 --> 00:55:02.116
And let's say that X is positive, then

00:55:02.116 --> 00:55:06.612
I want to increase WA bit and if I

00:55:06.612 --> 00:55:09.710
increase WA bit then I'm going to.

00:55:10.070 --> 00:55:13.230
Increase my score or increase like the

00:55:13.230 --> 00:55:16.060
output of my linear model, which will

00:55:16.060 --> 00:55:18.380
then better satisfy the margin.

00:55:21.030 --> 00:55:23.160
And then I'm going to take.

00:55:23.160 --> 00:55:25.380
So this is just the gradient here

00:55:25.380 --> 00:55:27.760
Lambda times W Plus this thing that I

00:55:27.760 --> 00:55:28.740
just talked about.

00:55:30.920 --> 00:55:32.630
So we're going to use this to do what's

00:55:32.630 --> 00:55:34.300
called gradient descent.

00:55:35.500 --> 00:55:37.820
SGD stands for stochastic gradient

00:55:37.820 --> 00:55:38.310
descent.

00:55:39.280 --> 00:55:41.050
And I'll explain what stochastic, why

00:55:41.050 --> 00:55:43.420
it's stochastic, and a little bit.

00:55:43.420 --> 00:55:45.690
But this is like a nice illustration of

00:55:45.690 --> 00:55:47.990
gradient descent, basically.

00:55:48.700 --> 00:55:50.213
You visualize.

00:55:50.213 --> 00:55:52.600
You can mentally visualize it as you've

00:55:52.600 --> 00:55:53.270
got some.

00:55:54.370 --> 00:55:56.200
You've got some surface of your loss

00:55:56.200 --> 00:55:58.070
function, so depending on what your

00:55:58.070 --> 00:55:59.630
model is, you would have different

00:55:59.630 --> 00:56:00.220
losses.

00:56:00.950 --> 00:56:02.500
And so here it's just like if your

00:56:02.500 --> 00:56:04.600
model just has two parameters, then you

00:56:04.600 --> 00:56:07.400
can visualize this as like a 3D surface

00:56:07.400 --> 00:56:09.070
where the height is your loss.

00:56:09.730 --> 00:56:13.420
And the position XY position on this is

00:56:13.420 --> 00:56:14.950
the parameters.

00:56:16.390 --> 00:56:17.730
And gradient descent, you're just

00:56:17.730 --> 00:56:19.269
trying to roll down the hill.

00:56:19.270 --> 00:56:20.590
That's why I had a ball rolling down

00:56:20.590 --> 00:56:21.950
the hill on the first slide.

00:56:22.510 --> 00:56:25.710
And you try to every position you

00:56:25.710 --> 00:56:26.990
calculate gradient.

00:56:26.990 --> 00:56:29.070
That's the direction of the slope and

00:56:29.070 --> 00:56:29.830
its speed.

00:56:30.430 --> 00:56:32.240
And then you take a little step in the

00:56:32.240 --> 00:56:34.020
direction of that gradient downward.

00:56:35.560 --> 00:56:38.370
And there's a common terms that you'll

00:56:38.370 --> 00:56:40.532
hear in this kind of optimization are

00:56:40.532 --> 00:56:43.300
like global optimum and local optimum.

00:56:43.300 --> 00:56:45.956
So a global optimum is the lowest point

00:56:45.956 --> 00:56:48.780
in the whole like surface of solutions.

00:56:49.890 --> 00:56:51.660
That's where you want to go in.

00:56:51.660 --> 00:56:54.606
A local optimum means that if you have

00:56:54.606 --> 00:56:56.960
that solution then you can't improve it

00:56:56.960 --> 00:56:58.840
by taking a small step anywhere.

00:56:58.840 --> 00:57:00.460
So you have to go up the hill before

00:57:00.460 --> 00:57:01.320
you can go down the hill.

00:57:02.030 --> 00:57:04.613
So this is a global optimum here and

00:57:04.613 --> 00:57:06.329
this is a local optimum.

00:57:06.330 --> 00:57:09.720
Now SVMS, SVMS are just like a big

00:57:09.720 --> 00:57:10.430
bowl.

00:57:10.430 --> 00:57:11.650
They are convex.

00:57:11.650 --> 00:57:13.810
It's a convex problem where they're the

00:57:13.810 --> 00:57:15.820
only local optimum is global optimum.

00:57:16.960 --> 00:57:18.620
And so with the suitable optimization

00:57:18.620 --> 00:57:20.090
algorithm you should always be able to

00:57:20.090 --> 00:57:21.540
find the best solution.

00:57:22.320 --> 00:57:25.260
But neural networks, which we'll get to

00:57:25.260 --> 00:57:28.460
later, are like really bumpy, and so

00:57:28.460 --> 00:57:29.870
the optimization is much harder.

00:57:33.810 --> 00:57:36.080
So finally, this is the Pegasus

00:57:36.080 --> 00:57:38.380
algorithm for stochastic gradient

00:57:38.380 --> 00:57:38.920
descent.

00:57:39.910 --> 00:57:40.490
And.

00:57:41.120 --> 00:57:43.309
Fortunately, it's kind of it's kind of

00:57:43.310 --> 00:57:46.490
short, it's a simple algorithm, but it

00:57:46.490 --> 00:57:47.790
takes a little bit of explanation.

00:57:48.710 --> 00:57:50.200
Just laughing because my daughter has

00:57:50.200 --> 00:57:52.720
this book, fortunately, unfortunately,

00:57:52.720 --> 00:57:53.360
where?

00:57:54.040 --> 00:57:57.710
Fortunately, unfortunately, the he gets

00:57:57.710 --> 00:57:58.100
an airplane.

00:57:58.100 --> 00:58:00.041
The engine exploded, fortunately at a

00:58:00.041 --> 00:58:00.353
parachute.

00:58:00.353 --> 00:58:02.552
Unfortunately there is a hole in the

00:58:02.552 --> 00:58:02.933
parachute.

00:58:02.933 --> 00:58:05.110
Fortunately there is a haystack below

00:58:05.110 --> 00:58:05.380
him.

00:58:05.380 --> 00:58:07.500
Unfortunately there is a pitchfork in

00:58:07.500 --> 00:58:08.080
haystack.

00:58:08.080 --> 00:58:09.490
Just goes on like that for the whole

00:58:09.490 --> 00:58:10.010
book.

00:58:10.990 --> 00:58:12.700
It's really funny, so fortunately this

00:58:12.700 --> 00:58:13.420
is short.

00:58:13.420 --> 00:58:15.490
Unfortunately, it still may be hard to

00:58:15.490 --> 00:58:16.190
understand.

00:58:16.990 --> 00:58:18.760
And so the.

00:58:18.760 --> 00:58:21.250
So we have a training set here.

00:58:21.250 --> 00:58:23.280
These are the input training examples.

00:58:23.940 --> 00:58:25.950
I've got some regularization weight and

00:58:25.950 --> 00:58:27.380
I have some number of iterations that

00:58:27.380 --> 00:58:28.030
I'm going to do.

00:58:28.850 --> 00:58:30.370
And I initialize the weights to be

00:58:30.370 --> 00:58:31.120
zeros.

00:58:31.120 --> 00:58:32.630
These are the weights in my model.

00:58:33.290 --> 00:58:35.220
And then I step through each iteration.

00:58:36.070 --> 00:58:38.270
And I choose some sample.

00:58:39.280 --> 00:58:41.140
Uniformly at random, so I just choose

00:58:41.140 --> 00:58:43.170
one single training sample from my data

00:58:43.170 --> 00:58:43.480
set.

00:58:44.310 --> 00:58:48.440
And then I set my learning rate which

00:58:48.440 --> 00:58:49.100
is.

00:58:49.180 --> 00:58:49.790


00:58:52.030 --> 00:58:54.220
Or I should say, I guess that's it.

00:58:54.220 --> 00:58:55.720
So I choose some samples from my data

00:58:55.720 --> 00:58:56.220
set.

00:58:56.220 --> 00:58:57.840
Then I set my learning rate which is

00:58:57.840 --> 00:59:00.520
one over Lambda T so basically my step

00:59:00.520 --> 00:59:02.945
size is going to get smaller the more

00:59:02.945 --> 00:59:04.200
samples that I process.

00:59:06.200 --> 00:59:10.200
And if my margin is less than one, that

00:59:10.200 --> 00:59:12.330
means that I'm not happy with my score

00:59:12.330 --> 00:59:13.330
for that example.

00:59:14.120 --> 00:59:16.990
So I increment my weights by 1 minus

00:59:16.990 --> 00:59:20.828
ETA Lambda W so this is the.

00:59:20.828 --> 00:59:22.833
This part is just saying that I want my

00:59:22.833 --> 00:59:24.160
weights to get smaller in general

00:59:24.160 --> 00:59:25.760
because I'm trying to minimize the

00:59:25.760 --> 00:59:27.760
squared weights and that's based on the

00:59:27.760 --> 00:59:29.570
derivative of W transpose W.

00:59:30.480 --> 00:59:32.370
And then this part is saying I also

00:59:32.370 --> 00:59:34.180
want to improve my score for this

00:59:34.180 --> 00:59:36.110
example, so I add.

00:59:37.400 --> 00:59:44.440
I add ETA YX so if X is positive then

00:59:44.440 --> 00:59:46.712
I'm going to increase and Y is

00:59:46.712 --> 00:59:48.340
positive, then I'm going to increase

00:59:48.340 --> 00:59:50.790
the weight so that it becomes so that X

00:59:50.790 --> 00:59:51.920
becomes more positive.

00:59:52.550 --> 00:59:54.970
Is positive and Y is negative, then I'm

00:59:54.970 --> 00:59:57.438
going to decrease the weight so that so

00:59:57.438 --> 00:59:59.634
that X becomes less positive, more

00:59:59.634 --> 01:00:00.940
negative and more correct.

01:00:02.430 --> 01:00:04.410
And then if I'm happy with my score of

01:00:04.410 --> 01:00:06.830
the example, it's outside the margin YW

01:00:06.830 --> 01:00:07.750
transpose X.

01:00:08.950 --> 01:00:12.040
Is greater or equal to 1, then I only

01:00:12.040 --> 01:00:13.750
care about this regularization term, so

01:00:13.750 --> 01:00:15.010
I'm just going to make the weight a

01:00:15.010 --> 01:00:17.100
little bit smaller because I'm trying

01:00:17.100 --> 01:00:18.590
to again minimize the square of the

01:00:18.590 --> 01:00:18.850
weights.

01:00:20.220 --> 01:00:21.500
So I just that's it.

01:00:21.500 --> 01:00:23.145
I just stepped through all the

01:00:23.145 --> 01:00:23.420
examples.

01:00:23.420 --> 01:00:25.615
It's like a pretty short optimization.

01:00:25.615 --> 01:00:27.750
And what I'm doing is I'm just like

01:00:27.750 --> 01:00:30.530
incrementally trying to improve my

01:00:30.530 --> 01:00:32.479
solution for each example that I

01:00:32.480 --> 01:00:33.490
encounter.

01:00:33.490 --> 01:00:37.459
And what's not intuitive maybe is that

01:00:37.460 --> 01:00:38.810
theoretically you can show that this

01:00:38.810 --> 01:00:42.970
eventually improves gives you the best

01:00:42.970 --> 01:00:44.860
possible weights for all your examples.

01:00:47.930 --> 01:00:49.640
There's a there's another version of

01:00:49.640 --> 01:00:52.180
this where you use what's called a mini

01:00:52.180 --> 01:00:52.770
batch.

01:00:53.580 --> 01:00:55.290
We're just instead of sampling.

01:00:55.290 --> 01:00:57.165
Instead of taking one sample at a time,

01:00:57.165 --> 01:00:59.165
one training sample at a time, you take

01:00:59.165 --> 01:01:01.280
a whole set at a time of random set of

01:01:01.280 --> 01:01:01.930
examples.

01:01:03.000 --> 01:01:06.970
And then you take instead of instead of

01:01:06.970 --> 01:01:09.660
this term involving like the margin

01:01:09.660 --> 01:01:13.570
loss of one example involves the

01:01:13.570 --> 01:01:16.564
average of those losses for all the

01:01:16.564 --> 01:01:17.999
examples that violate the margin.

01:01:18.000 --> 01:01:23.340
So you're taking the average of YXI

01:01:23.340 --> 01:01:24.750
where these are the examples in your

01:01:24.750 --> 01:01:26.530
mini batch that violate the margin.

01:01:27.200 --> 01:01:29.270
And multiplying by ETA and adding it to

01:01:29.270 --> 01:01:29.640
West.

01:01:30.740 --> 01:01:32.470
So if your batch size is 1, it's the

01:01:32.470 --> 01:01:34.900
exact same algorithm as before, but by

01:01:34.900 --> 01:01:36.600
averaging your gradient over multiple

01:01:36.600 --> 01:01:38.220
examples you get a more stable

01:01:38.220 --> 01:01:39.230
optimization.

01:01:39.230 --> 01:01:41.250
And it can also be faster if you're

01:01:41.250 --> 01:01:44.800
able to parallelize your algorithm like

01:01:44.800 --> 01:01:47.470
you can with multiple GPUs, I mean CPUs

01:01:47.470 --> 01:01:48.120
or GPU.

01:01:52.450 --> 01:01:53.580
Any questions about that?

01:01:55.250 --> 01:01:55.480
Yeah.

01:01:56.770 --> 01:01:57.310
When it comes to.

01:01:58.820 --> 01:02:01.350
Divide the regular regularization

01:02:01.350 --> 01:02:02.740
constant by the mini batch.

01:02:04.020 --> 01:02:05.420
An.

01:02:05.770 --> 01:02:07.330
Just into when you're updating the

01:02:07.330 --> 01:02:07.680
weights.

01:02:10.790 --> 01:02:12.510
The average of that badge is not just

01:02:12.510 --> 01:02:15.110
like stochastic versus 1, right?

01:02:15.110 --> 01:02:17.145
So are you saying should you be taking

01:02:17.145 --> 01:02:19.781
like a bigger, are you saying should

01:02:19.781 --> 01:02:21.789
you change like how much weight you

01:02:21.790 --> 01:02:25.350
assign to this guy where you're trying

01:02:25.350 --> 01:02:26.350
to reduce the weight?

01:02:28.150 --> 01:02:30.930
Divided by the batch size by bad.

01:02:32.390 --> 01:02:32.960
This update.

01:02:34.290 --> 01:02:36.460
After 10 and then so you divide it by

01:02:36.460 --> 01:02:36.970
10.

01:02:36.970 --> 01:02:37.370
OK.

01:02:38.230 --> 01:02:39.950
You could do that.

01:02:39.950 --> 01:02:41.090
I mean this also.

01:02:41.090 --> 01:02:42.813
You don't have to have a 1 / K here,

01:02:42.813 --> 01:02:44.540
this could be just the sum.

01:02:44.540 --> 01:02:47.270
So here they averaged out the

01:02:47.270 --> 01:02:48.240
gradients.

01:02:48.300 --> 01:02:48.930
And.

01:02:49.910 --> 01:02:53.605
And also like sometimes, depending on

01:02:53.605 --> 01:02:56.210
your batch size, your ideal learning

01:02:56.210 --> 01:02:58.040
rate and other regularizations can

01:02:58.040 --> 01:02:58.970
sometimes change.

01:03:03.220 --> 01:03:07.570
So we saw SGD stochastic gradient

01:03:07.570 --> 01:03:10.420
descent for the hinge loss with, which

01:03:10.420 --> 01:03:11.740
is what the SVM uses.

01:03:13.340 --> 01:03:15.110
It's nice for the hinge loss because

01:03:15.110 --> 01:03:17.155
there's no gradient for incorrect or

01:03:17.155 --> 01:03:19.020
for confidently correct examples, so

01:03:19.020 --> 01:03:21.280
you only have to optimize over the ones

01:03:21.280 --> 01:03:22.310
that are within the margin.

01:03:24.320 --> 01:03:27.270
But you can also compute the gradients

01:03:27.270 --> 01:03:29.265
for all these other kinds of losses,

01:03:29.265 --> 01:03:30.830
like whoops, like the logistic

01:03:30.830 --> 01:03:32.810
regression loss or sigmoid loss.

01:03:35.540 --> 01:03:37.620
Another logistic loss, another kind of

01:03:37.620 --> 01:03:39.260
margin loss.

01:03:39.260 --> 01:03:40.730
These are not things that you should

01:03:40.730 --> 01:03:41.400
ever memorize.

01:03:41.400 --> 01:03:42.570
Or you can memorize them.

01:03:42.570 --> 01:03:44.470
I won't hold it against you, but.

01:03:45.510 --> 01:03:46.850
But you can always look them up, so

01:03:46.850 --> 01:03:47.820
they're not things you need to

01:03:47.820 --> 01:03:48.160
memorize.

01:03:50.430 --> 01:03:53.380
I will never ask you like what is the?

01:03:53.380 --> 01:03:55.270
I won't ask you like what's the

01:03:55.270 --> 01:03:56.600
gradient of some function.

01:03:58.090 --> 01:03:58.750
And.

01:03:59.660 --> 01:04:02.980
So this is just comparing like the

01:04:02.980 --> 01:04:05.930
optimization speed of the of this

01:04:05.930 --> 01:04:08.160
approach, Pegasus versus other

01:04:08.160 --> 01:04:08.900
optimizers.

01:04:10.000 --> 01:04:14.040
So for example, here's Pegasus.

01:04:14.040 --> 01:04:17.680
It goes like this is time on the X axis

01:04:17.680 --> 01:04:18.493
in seconds.

01:04:18.493 --> 01:04:20.920
So basically you want to get low

01:04:20.920 --> 01:04:22.300
because this is the objective that

01:04:22.300 --> 01:04:23.670
you're trying to minimize.

01:04:23.670 --> 01:04:25.900
So basically Pegasus shoots down to

01:04:25.900 --> 01:04:28.210
zero and like milliseconds and these

01:04:28.210 --> 01:04:29.980
other things are like still chugging

01:04:29.980 --> 01:04:31.940
away like many seconds later.

01:04:33.020 --> 01:04:33.730
And.

01:04:34.530 --> 01:04:37.500
And so consistently if you compare

01:04:37.500 --> 01:04:40.500
Pegasus to SVM perf, which is like

01:04:40.500 --> 01:04:41.920
stands for performance.

01:04:41.920 --> 01:04:45.050
It was a highly optimized SVM library.

01:04:45.940 --> 01:04:49.230
Or LA SVM, which I forget what that

01:04:49.230 --> 01:04:50.030
stands for right now.

01:04:50.740 --> 01:04:53.140
But two different SVM optimizers.

01:04:53.140 --> 01:04:56.056
Pegasus is just way faster you reach

01:04:56.056 --> 01:04:59.710
the you reach the ideal solution really

01:04:59.710 --> 01:05:00.690
really fast.

01:05:02.020 --> 01:05:04.290
The other one that performs just as

01:05:04.290 --> 01:05:06.280
well, if not better.

01:05:06.280 --> 01:05:09.180
Sdca is also a stochastic gradient

01:05:09.180 --> 01:05:13.470
descent method that just also chooses

01:05:13.470 --> 01:05:15.160
the learning rate dynamically instead

01:05:15.160 --> 01:05:16.738
of following a single schedule.

01:05:16.738 --> 01:05:19.080
The learning rate is the step size.

01:05:19.080 --> 01:05:20.460
It's like how much you move in the

01:05:20.460 --> 01:05:21.290
gradient direction.

01:05:24.240 --> 01:05:26.340
And then in terms of the error,

01:05:26.340 --> 01:05:28.440
training time and error, so it's so

01:05:28.440 --> 01:05:30.590
Pegasus is taking like under a second

01:05:30.590 --> 01:05:32.710
for all these different problems where

01:05:32.710 --> 01:05:34.390
some other libraries could take even

01:05:34.390 --> 01:05:35.380
hundreds of seconds.

01:05:36.290 --> 01:05:39.620
And it achieves just as good, if not

01:05:39.620 --> 01:05:42.120
better, error than most of them.

01:05:43.000 --> 01:05:44.800
And in part that's just like even

01:05:44.800 --> 01:05:46.090
though it's a global objective

01:05:46.090 --> 01:05:47.520
function, you have to like choose your

01:05:47.520 --> 01:05:50.120
regularization parameters and other

01:05:50.120 --> 01:05:50.720
parameters.

01:05:51.460 --> 01:05:53.490
And you have to.

01:05:53.860 --> 01:05:56.930
It may be hard to tell when you

01:05:56.930 --> 01:05:58.560
converge exactly, so you can get small

01:05:58.560 --> 01:06:00.180
differences between different

01:06:00.180 --> 01:06:00.800
algorithms.

01:06:04.300 --> 01:06:05.630
And then they also did.

01:06:05.630 --> 01:06:07.590
There's a kernelized version which

01:06:07.590 --> 01:06:07.873
won't.

01:06:07.873 --> 01:06:09.560
I won't go into, but it's the same

01:06:09.560 --> 01:06:10.350
principle.

01:06:10.770 --> 01:06:15.190
And so they're able to get.

01:06:15.300 --> 01:06:15.940


01:06:18.170 --> 01:06:20.520
They're able to use the kernelized

01:06:20.520 --> 01:06:21.960
version to get really good performance.

01:06:21.960 --> 01:06:24.470
So on MNIST for example, which was your

01:06:24.470 --> 01:06:29.010
homework one, they get 6% accuracy, 6%

01:06:29.010 --> 01:06:32.595
error rate using a kernelized SVM with

01:06:32.595 --> 01:06:33.460
a Gaussian kernel.

01:06:34.070 --> 01:06:35.330
So it's essentially just like a

01:06:35.330 --> 01:06:37.070
slightly smarter nearest neighbor

01:06:37.070 --> 01:06:37.850
algorithm.

01:06:40.840 --> 01:06:42.910
And the thing that's notable?

01:06:42.910 --> 01:06:44.210
Actually this takes.

01:06:48.920 --> 01:06:49.513
Kind of interesting.

01:06:49.513 --> 01:06:51.129
So it's not so fast.

01:06:51.130 --> 01:06:51.350
Sorry.

01:06:51.350 --> 01:06:52.590
It's just looking at the times.

01:06:52.590 --> 01:06:54.330
Yeah, so it's not so fast in the

01:06:54.330 --> 01:06:55.390
kernelized version, I guess.

01:06:55.390 --> 01:06:56.080
But it still works.

01:06:56.080 --> 01:06:57.650
I didn't look into that in depth, so

01:06:57.650 --> 01:06:57.965
I'm not.

01:06:57.965 --> 01:06:58.770
I can't explain it.

01:07:01.980 --> 01:07:02.290
Alright.

01:07:02.290 --> 01:07:04.050
And then finally like one other thing

01:07:04.050 --> 01:07:05.820
that they look at is the mini batch

01:07:05.820 --> 01:07:06.270
size.

01:07:06.270 --> 01:07:08.810
So if you as you like sample chunks of

01:07:08.810 --> 01:07:10.420
data and do the optimization with

01:07:10.420 --> 01:07:11.659
respect to each chunk of data.

01:07:12.730 --> 01:07:13.680
If you.

01:07:13.770 --> 01:07:14.430


01:07:15.530 --> 01:07:18.520
This is looking at the.

01:07:19.780 --> 01:07:22.780
At how close do you get to the ideal

01:07:22.780 --> 01:07:23.450
solution?

01:07:24.540 --> 01:07:26.830
And this is the mini batch size.

01:07:26.830 --> 01:07:28.860
So for a pretty big range of mini batch

01:07:28.860 --> 01:07:31.295
sizes you can get like very close to

01:07:31.295 --> 01:07:32.330
the ideal solution.

01:07:33.720 --> 01:07:36.570
So this is making an approximation

01:07:36.570 --> 01:07:39.660
because your every step you're choosing

01:07:39.660 --> 01:07:41.500
your step based on a subset of the

01:07:41.500 --> 01:07:41.860
data.

01:07:42.860 --> 01:07:47.530
But for like a big range of conditions,

01:07:47.530 --> 01:07:49.960
it gives you an ideal solution.

01:07:50.700 --> 01:07:53.459
And these are these are after different

01:07:53.460 --> 01:07:55.635
step length after different numbers of

01:07:55.635 --> 01:07:56.000
iterations.

01:07:56.000 --> 01:07:58.533
So if you do 4K iterations, you're at

01:07:58.533 --> 01:08:00.542
the Black line, 16 K iterations you're

01:08:00.542 --> 01:08:03.083
at the blue, and 64K iterations you're

01:08:03.083 --> 01:08:04.050
at the red.

01:08:05.030 --> 01:08:06.680
And yeah.

01:08:11.670 --> 01:08:13.200
And then they also did an experiment

01:08:13.200 --> 01:08:15.300
showing, like in their original paper,

01:08:15.300 --> 01:08:17.120
you would randomly sample with

01:08:17.120 --> 01:08:18.410
replacement the data.

01:08:18.410 --> 01:08:20.420
But if you randomly sample, if you just

01:08:20.420 --> 01:08:22.100
shuffle your data, essentially for

01:08:22.100 --> 01:08:23.750
what's called a epoch, which is like

01:08:23.750 --> 01:08:25.780
one cycle through the data, then you do

01:08:25.780 --> 01:08:26.250
better.

01:08:26.250 --> 01:08:28.680
So that's All in all optimization

01:08:28.680 --> 01:08:30.367
algorithms that I see now, you

01:08:30.367 --> 01:08:32.386
essentially shuffle the data, iterate

01:08:32.386 --> 01:08:35.440
through all the data and then reshuffle

01:08:35.440 --> 01:08:37.360
it and iterate again and each of those

01:08:37.360 --> 01:08:37.920
iterations.

01:08:37.970 --> 01:08:39.490
To the data is called at epoch.

01:08:41.110 --> 01:08:41.750
Epic.

01:08:41.750 --> 01:08:42.760
I never know how to pronounce it.

01:08:44.260 --> 01:08:46.440
And then they also just showed like

01:08:46.440 --> 01:08:48.363
their learning rate schedule seems to

01:08:48.363 --> 01:08:50.150
like provide much more stable results

01:08:50.150 --> 01:08:51.750
compared to a previous approach that

01:08:51.750 --> 01:08:53.540
would use a fixed learning rate for all

01:08:53.540 --> 01:08:55.200
the for all the iterations.

01:08:58.610 --> 01:09:02.230
So, takeaways and surprising facts

01:09:02.230 --> 01:09:03.190
about Pegasus.

01:09:04.460 --> 01:09:08.480
So it's using this SGD, which could be

01:09:08.480 --> 01:09:11.730
an acronym for sub gradient descent or

01:09:11.730 --> 01:09:13.560
stochastic gradient descent, and it

01:09:13.560 --> 01:09:14.780
applies both ways here.

01:09:15.580 --> 01:09:16.585
It's very simple.

01:09:16.585 --> 01:09:18.160
It's an effective optimization

01:09:18.160 --> 01:09:18.675
algorithm.

01:09:18.675 --> 01:09:20.830
It's probably the most widely used

01:09:20.830 --> 01:09:22.640
optimization algorithm in machine

01:09:22.640 --> 01:09:22.990
learning.

01:09:24.330 --> 01:09:26.230
There's very many variants of it, so

01:09:26.230 --> 01:09:28.590
I'll talk about some like atom in a

01:09:28.590 --> 01:09:30.990
couple classes, but the idea is that

01:09:30.990 --> 01:09:32.540
you just step towards a better solution

01:09:32.540 --> 01:09:34.380
of your parameters based on a small

01:09:34.380 --> 01:09:35.830
sample of the training data

01:09:35.830 --> 01:09:36.550
iteratively.

01:09:37.490 --> 01:09:39.370
It's not very sensitive that mini batch

01:09:39.370 --> 01:09:39.940
size.

01:09:40.990 --> 01:09:43.140
With larger batches you get like more

01:09:43.140 --> 01:09:44.720
stable estimates to the gradient and it

01:09:44.720 --> 01:09:46.560
can be a lot faster if you're doing GPU

01:09:46.560 --> 01:09:47.430
processing.

01:09:47.430 --> 01:09:50.860
So in machine learning and like large

01:09:50.860 --> 01:09:53.470
scale machine learning, deep learning.

01:09:54.150 --> 01:09:56.790
You tend to prefer large batches up to

01:09:56.790 --> 01:09:58.520
what you're GPU memory can hold.

01:09:59.680 --> 01:10:01.620
The same learning schedule is effective

01:10:01.620 --> 01:10:04.120
across many problems, so they're like

01:10:04.120 --> 01:10:05.865
decreasing the learning rate gradually

01:10:05.865 --> 01:10:08.610
is just like generally a good way to

01:10:08.610 --> 01:10:08.900
go.

01:10:08.900 --> 01:10:10.780
It doesn't require a lot of tuning.

01:10:12.550 --> 01:10:15.070
And the thing, so I don't know if it's

01:10:15.070 --> 01:10:17.350
in this paper, but this I forgot to

01:10:17.350 --> 01:10:18.880
mention, this work was done at TTI

01:10:18.880 --> 01:10:21.345
Chicago, so just very new here.

01:10:21.345 --> 01:10:23.890
So one of the first talks they give was

01:10:23.890 --> 01:10:25.540
for our group at UIUC.

01:10:25.540 --> 01:10:27.190
So I remember I remember them talking

01:10:27.190 --> 01:10:27.490
about it.

01:10:28.360 --> 01:10:29.810
And one of the things that's kind of a

01:10:29.810 --> 01:10:30.820
surprising result.

01:10:31.650 --> 01:10:35.390
Is that with this algorithm it's faster

01:10:35.390 --> 01:10:37.880
to train using a larger training set,

01:10:37.880 --> 01:10:40.180
so that's not super intuitive, right?

01:10:41.370 --> 01:10:42.710
In order to get the same test

01:10:42.710 --> 01:10:43.430
performance.

01:10:43.430 --> 01:10:46.990
And the reason is like if you think

01:10:46.990 --> 01:10:49.780
about like a little bit of data, if you

01:10:49.780 --> 01:10:51.970
have a little bit of data, then you

01:10:51.970 --> 01:10:53.540
have to like keep on iterating over

01:10:53.540 --> 01:10:55.450
that same little bit of data and each

01:10:55.450 --> 01:10:57.010
time you iterate over it, you're just

01:10:57.010 --> 01:10:58.330
like learning a little bit new.

01:10:58.330 --> 01:10:59.660
It's like trying to keep on like

01:10:59.660 --> 01:11:00.999
squeezing the same water out of a

01:11:01.000 --> 01:11:01.510
sponge.

01:11:02.560 --> 01:11:04.557
But if you have a lot of data and

01:11:04.557 --> 01:11:06.270
you're cycling through this big thing

01:11:06.270 --> 01:11:08.030
of data, you keep on seeing new things

01:11:08.030 --> 01:11:10.125
as you as you go through the data.

01:11:10.125 --> 01:11:12.290
And so you're learning more, like

01:11:12.290 --> 01:11:14.050
learning more per time.

01:11:14.690 --> 01:11:17.719
So if you have a million examples then,

01:11:17.719 --> 01:11:20.520
and you do like a million steps with

01:11:20.520 --> 01:11:22.220
one example each, then you learn a lot

01:11:22.220 --> 01:11:22.930
new.

01:11:22.930 --> 01:11:25.257
But if you have 10 examples and you do

01:11:25.257 --> 01:11:26.829
a million steps, million steps, then

01:11:26.830 --> 01:11:28.955
you've just seen there's 10 examples

01:11:28.955 --> 01:11:29.830
10,000 times.

01:11:30.660 --> 01:11:32.410
Or something 100,000 times.

01:11:32.410 --> 01:11:36.630
So if you get a larger training set,

01:11:36.630 --> 01:11:38.440
you actually get faster.

01:11:38.440 --> 01:11:40.230
It's faster to get the same test

01:11:40.230 --> 01:11:41.840
performance.

01:11:41.840 --> 01:11:44.020
And where that comes into play is that

01:11:44.020 --> 01:11:45.978
sometimes I'll have somebody say like,

01:11:45.978 --> 01:11:48.355
I don't like, I don't want to, I don't

01:11:48.355 --> 01:11:49.939
want to get more training examples

01:11:49.940 --> 01:11:51.700
because my optimization will take too

01:11:51.700 --> 01:11:52.390
long.

01:11:52.390 --> 01:11:54.650
But actually your optimization will be

01:11:54.650 --> 01:11:56.116
faster if you have more training

01:11:56.116 --> 01:11:57.500
examples, if you're using this kind of

01:11:57.500 --> 01:11:59.090
approach, if what you're trying to do

01:11:59.090 --> 01:12:01.490
is maximize your performance.

01:12:01.550 --> 01:12:02.780
Which is pretty much what you're always

01:12:02.780 --> 01:12:03.160
trying to do.

01:12:04.090 --> 01:12:06.810
So larger training set means faster

01:12:06.810 --> 01:12:07.920
runtime for training.

01:12:10.280 --> 01:12:14.330
So that's all about SVMS and SGDS.

01:12:14.330 --> 01:12:16.640
I know that's a lot to take in, but

01:12:16.640 --> 01:12:18.270
thank you for being patient and

01:12:18.270 --> 01:12:18.590
listening.

01:12:19.300 --> 01:12:21.480
And next week I'm going to start

01:12:21.480 --> 01:12:22.440
talking about neural networks.

01:12:22.440 --> 01:12:23.990
So I'll talk about multilayer

01:12:23.990 --> 01:12:26.450
perceptrons and then some concepts and

01:12:26.450 --> 01:12:28.120
deep networks.

01:12:28.120 --> 01:12:28.800
Thank you.

01:12:28.800 --> 01:12:30.040
Have a good weekend.