CS-498 Applied Machine Learning
D.A. Forsyth --- 3310 Siebel Center
Office Hours Time: WF 14h00-15h00, Location: 3310 Siebel
Alternative locations may be available
- Tanmay Gangwani firstname.lastname@example.org
- Tiantian Fang email@example.com
Important; Important; Important
Announcements page - check this frequently!
LINK ISN'T BROKEN I will be absent 31 Jan (sorry!). Also absent 21 Feb, aargh!
Check for movies!
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.
- DAF: 14h00-15h00 WF
- Tanmay Gangwani Mon, 10h00-11h00; Thur 10h00-11h00 Siebel 0207.
- Tiantian Fang Tue, 15h30-17h30 Siebel 0207.
Evaluation is by: Homeworks and take home final.
I will shortly post a policy on collaboration and plagiarism
please complete this. I intend it to be anonymous (but don't fully understand google forms, so...) and it will help me know what you know already. Please don't fill in lots of forms to bias, etc.
A total of 11 homeworks will appear here. There will be no final exam
- one homework will be designated a take-home final.
I will post drafts of the homeworks here, when we're fairly sure what we want you
to do, but are working out submission details, etc.
Please submit a single PDF to Gradescope. It should contain your solutions to all the problems included in the homework. If the homework involves coding, please include the code in the same PDF (at the end, as an Appendix). This makes the life of graders easy. If there’s too much code, such that the PDF size exceeds the maximum allowed on Gradescope, submit a separate .zip for the code.
- Homework 1(here) due 10 Feb 2020. due date changed via piazza announcement to 17 Feb 23h59
- Homework 2(here) due 24 Feb 2020.Note date change!
- Homework 3(here) due 2 Mar 2020.
- Homework 4(here) due 30 Mar 2020.
I will start at the beginning of the textbook and
proceed to the end, covering approximately one chapter per week. You'll notice there are 19
substantive chapters and 15 weeks; this is to allow a little
spreading out, but in week N I expect to be close to chapter 15*N/19.
Read the textbook. I wrote it specifically for this
course, AND it's free. I will split time in lecture between sketching
important points described in the text, and solving problems. If you
haven't read the text, this might be quite puzzling.
Applied Machine Learning D.A. Forsyth, Springer, 2019
Important In the past, people have brought the pdf with
them on mobile devices. I think this is a good idea. Or you could buy a paper copy. The PDF is a
free download from the UIUC library (you have
to be on the intranet to download it, I think)
I'm a video star! (or at least, I have been filmed)
- you can see me here though you'll need to log in, and it may take a short while after class to be ready
|| low-resClassification; Nearest neighbors
|| low-resNaive Bayes; SVMs
|| low-resLearning theory
|| low-resHigh dimensional data; Covariance matrices
|| low-resDiagonalizing covariance matrices; multivariate normals
|| low-resPrincipal components
|| low-resMore PCA; NIPALS
|| low-resLow rank models
|| low-rescanonical correlation analysis
Probability and Statistics for Computer Science,
- I can no longer release a PDF, as this has been published. The
moire effect on the cover picture is the result of my scanner
interacting with a shiny cover.
Code fragments I showed in class:
I've cleaned some of these up a bit, but they're not intended to be production code, etc;
just to show some R tricks. Among other things, these codes contain
- A naive bayes classifier on the Pima indians dataset; I averaged over 10 test train splits, and ignored examples with NA values; mainly interesting for simple code tricks. File here.
- A naive bayes classifier on the Pima indians dataset; I averaged over 10 test train splits, but now I used examples with NA values both in train and test; mainly interesting for simple code tricks. File here.
- A naive bayes classifier on the Pima indians dataset, using Klar and Caret; mainly interesting for simple code tricks. File here.
- An SVM on the Pima indians dataset, using Klar and Caret and SVMLight; mainly interesting for simple code tricks. File here.
- A much more elaborate SVM on the Pima indians dataset, using Klar and Caret and SVMLight. File here.