CS-498 Applied Machine Learning
D.A. Forsyth --- 3310 Siebel Center
Trevor Walker --- 4207 Siebel Center
Office Hours Time: TBA, Location: TBA
Alternative locations may be available
- Lavisha Aggarwal
- Jyoti Aneja
- Xiaoyang Bai
- Shruti Bhargava
- Anand Bhattad
- Krishna Dusad
- Ji Li
- Qixuan Li
Important; Important; Important
Announcements page - check this frequently!
LINK ISN'T BROKEN I will be absent 25, 27 Sep, and FOR SURE 2 Oct (sorry!).
This page gives
movies to review in place of morning lecture. Afternoon lecture with Trevor Walker will proceed as usual.
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 to take the class, or something. I'll try to
admit everyone, but can't concentrate on doing that if all try to tell
me why they want to get in.
No 4-hour version. Sorry, too
many students and versions already.
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: TBA
- TW: TBA
- Lavisha Aggarwal: Fri 16h00-17h00, Mon 11h00-12h00
- Jyoti Aneja: Thur 18h00-19h00, Fri 11h00-12h00
- Xiaoyang Bai: Thur 10h00-11h00, Fri 17h00-18h00
- Shruti Bhargava: Mon 16h00-17h00, Wed 15h00-16h00
- Anand Bhattad: Tue 17h00-18h00, Wed 10h00-11h00
- Krishna Dusad: Tue 10h00-11h00, Wed 16h00-17h00
- Ji Li: Thur 16:00 - 17:00,
Fri 10:00 - 11:00
- Qixuan Li: Thur 13h00-14h00, Fri 13h00-14h00
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.
- Homework 1 Due 23h59, Mon 10 Sep Note submission details on page.
- Homework 2 Due 23h59, Mon 17 Sep
- Homework 3 Due 23h59, Mon 24 Sep
- Homework 4 Due 23h59, Mon 2 Oct Notice you can now do this in pairs
- Homework 5 Due 23h59, Mon 16 Oct Notice you can do this in triples
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.
- Homework 6 Due 23h59, Mon 22 Oct Notice you can do this in pairs
- Homework 7 Due 23h59, Mon 29 Oct Notice you can do this in triples
- Homework 8 Due 23h59, Mon 12 Nov Notice you can do this in triples
- Homework 9 Due 23h59, Mon 3 Dec Notice you can do this in triples
- Homework 10 Due 23h59, Mon 10 Dec Notice you can do this in triples
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 14
substantive chapters and 15 weeks; this is to allow a little
spreading out, but in week N I expect to be in or close to chapter N.
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.
Important This will change as I
purge typos, etc. In the past, people have brought the pdf with
them on mobile devices. If you print it, you'll have to do it again.
Applied Machine Learning, D.A. Forsyth, (approximate 12'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 13'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 14'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 15'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 16'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 16'th draft)
Applied Machine Learning, D.A. Forsyth, (approximate 17'th draft)
this course (which is now right, I believe)
Piazza duty list
Generally, TA's are expected to spend much of their time on
piazza. But we have a system of on-duty and off-duty days. On these days, you'll find
much of the piazza action from the named TA (we hope!)
- Monday: Anand, Shruti
- Tuesday: Anand, Xiaoyang
- Wednesday: Lavisha, Krishna
- Thursday: Lavisha, Xiaoyang, Ji
- Friday: Jyoti, Shruti
- Saturday: Krishna, Jyoti
- Sunday: Qixuan
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
Vintage movies from the dawn of time
broken links fixed (I think)
low resolution movies of the class screen; good motivation to
remind me to adjust zoom on projector, swap screens, etc.
higher resolution movies of the class screen, for those who like more pixels.
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.