CS-498 Applied Machine Learning

D.A. Forsyth --- 3310 Siebel Center

Office Hours Time: WF 14h00-15h00, Location: 3310 Siebel

DAF hangs in sidemount scuba gear, with left tank in slightly poor trim, over a small submerged wreck interior of a sunken bus, in gloomy lighting, with DAF in sidemount scuba gear looking to the right of view, and pointing a light

Alternative locations may be available

TA's:

Important; Important; Important

We are no longer meeting in person. I will release movies, readings and homeworks to keep the course running. You should have received email from me about this.

Key points:

Grading and cutoffs

I and the TA's have received repeated questions about the precise location of grade cutoffs. I won't bind myself answering these, but here is some general advice: if you've done the homework, worry about something else. It’s my intention that all students who do all homework reasonably well will get an A; experience with the class in the past has been that meant about 2/3 of students did so, about 1/4 were in the Bs, and the rest were scattered cause they didn’t do their homework.

Four hour vs Three hour version of the course

If you're doing the four hour version of the course, you must do the extra homework, below. This is at peril of not having done all the homework. It's informative, and not hard.

Course Content 13-Mar and on

Records of our meetings

Class Date Brief movie Chat
25 Mar Part of my intro to each of the days movies chat record
27 Mar Part of my intro to each of the days movies; this is ridiculously big (0.4G), sorry, I'll keep trying. chat record
01 Apr Part of my intro to each of the days movies. chat record
03 Apr Part of my intro to each of the days movies, with a fair amount of Q+A on the homeworks, particularly EM (big, sorry!). chat record
08 Apr Part of my intro to each of the days movies, with a fair amount of Q+A on the homeworks, particularly EM (big, sorry!). No chat to report
10 Apr Part of my intro to each of the days movies. I'm asked how to avoid ennui in these very trying times. Answer (which is right) I don't think you can; best you can do is manage it. As an exercise, would you think well of someone who wasn't troubled by what's going on? No chat to report
15 Apr My intro to each of the days movies. AND an answer to a question about graphical models. No chat to report
17 Apr Questions and answers. No chat to report
22 Apr Web page summary; questions and answers. AND Another question and answers. No chat to report
24 Apr Brief update AND Q+A AND more Q+A. No chat to report
29 Apr A lecture on image classifiers AND Q+A on variational derivation. No chat to report
1 May A Lecture on object detectors No chat to report
6 May A Lecture on TSNE, autoencoders and generative models, with Q+A No chat to report

Short Movies

Class Date Readings Movies
13 Mar ch. 11, up to 11.4 bias+variance (end of 11 Mar lecture)
13 Mar Simple model selection
13 Mar Robust regression using IRLS
13 Mar Regression using Generalized Linear Models
25 Mar Finish ch. 11 Regression using the Lasso
25 Mar Regression using Elastic Net
27 Mar 12.1 Greedy stagewise regression
27 Mar/1 April 12.2 Gradient boost
1 April 12.2 Gradient boosting decision stumps
1 April 12.2 Gradient boosting regression trees
3 April 13.1 Markov chains - basic ideas
3 April 13.2 Simulating a Markov Chain
3 April 13.2 Text models with Markov Chains
8 April 13.2 Hidden Markov Models -basic ideas
8 April 13.2 Hidden Markov Models - Dynamic Programming
8 April 13.2 Hidden Markov Models - an example
10 April 13.3 Learning an HMM from data using EM (sorry, no short movie)
15 April 14.1 Graphical Models
15 April 14.2 Conditional Random Fields
15 April 14.3 Setting up structure learning (this is optional)
15 April 14.3 Doing structure learning (this is optional)
17 April 15.1 Boltzmann machines
17 April 15.1 Discrete Markov Random Fields
17 April 15.2 Variational Free Energy
17 April 15.2, 15.3 Mean Field Inference
22 April 16.1 Deep nets: Simple units
22 April 16.1 (also read 16.2) Deep nets: Gradients
22 April 16.3 (also, read 16.4) Deep nets: Backpropagation
24 April 16.4 Deep nets: Gradient tricks
24 April 17.1 Deep nets: Convolutional layers
24 April 17.1 (read 17.2) Deep nets: More on convolutional layers
29 Apr 17.2, 18 Image classifiers
1 May 18 Object detectors

Long Movies

Class Date Readings Movies
13 Mar ch. 11 Bias+Variance; simple model selection; IRLS
13 Mar/25 Mar Generalized linear models; Lasso;
25 Mar More generalized linear models; Lasso; Elastic net; some other stuff which you can ignore
27 Mar, 1 April 12.1, 12.2 Boosting and Gradient Boost
3 April 13.1 Introductory Markov chains (the chapter reference is wrong - I changed the chapter numbers - it's an old movie)
3 April/8 April 13.2 and 13.3 Simulating Markov chains; text models; Hidden Markov Models; dynamic programming
10 April 13.3 Learning an HMM from data using EM
15 April 14.1,14.2, 14.3 Some graphical models, structure learning points (last 1/2 hour of the movie! not ideal, but what I have; short movies are better)
17 April 14.3, 15 Some more structure learning leading into mean field ideas (not ideal, but what I have; short movies are better).
22 April 16.1-16.2 Units, stochastic gradient descent, and building a simple classifier (not ideal, but what I have; short movies are better).
24 April 16.3-17.1 Backpropagation, convolutional layers and gradient tricks (not ideal, but what I have; short movies are better).
29 Apr 17.2, 18 Image classifiers (same as short movie)
1 May 18 Object detectors
6 May 19 A Lecture on TSNE, autoencoders and generative models, with Q+A

Announcements

Announcements page - check this frequently!

LINK ISN'T BROKEN I will be absent 31 Jan (sorry!). Also absent 21 Feb, aargh! Also absent 13 Mar, mild signs of illness so self-isolating, aargh! Check for movies!

Contact policy

I'm quite distracted and am focusing on content preparation. Generally, please do not bring DAF an issue you haven't already raised with a TA.

Questions I've been getting a lot

Getting into the class In the past, we've been able to admit everyone who wanted to get into the in-person version of the class after the first rush settled down. Will this be true this semester? who knows? not me. PLEASE do not come and tell me that you really want to get in, or your cat died and its last words were you should take the class, or something. We're not going to go over an enrollment of 100. Corollary: If you plan dropping, do so early; someone else wants your seat.

Can I get in even though I won't be able to come to lecture cause I'm doing something else, but I'll watch the movies. I think this strategy is unwise, but I suppose it's not really my problem.

Can I audit? The main resource limits on the physical class are physical seats in the room. We cannot have an overcrowded room. If physical seats are open, sure (I'm always happy to have an audience); but please don't take a seat that should be occupied by someone who is registered

Important contact advice

A really common question is: how do I do something in R? Usually, I get the answer to this by searching; I use Google, but you may have a preferred search. If you ask me or a TA this question, and we do this it in front of you successfully you should feel a little embarrassed cause you could have done this for yourself. Warning: we will embarrass you in this way; it's better to do this sort of thing for yourself.

Office Hours