CS-498 Applied Machine Learning
D.A. Forsyth --- 3310 Siebel Center
13:00 - 14:15 OR 1.00 pm-2.15 pm (in old fashioned time)
1404 Siebel Center
Tanmay Gangwani firstname.lastname@example.org
Jiajun Lu email@example.com
Jason Rock firstname.lastname@example.org
Anirud Yadav email@example.com
- DAF: Mon 10-11 and Fri 2:30 - 3:30
- Jason: Mon 11-12 and Tues 1:30 - 2:30
- Jiajun: Tues 2:30 - 3:30 and Wed 5 - 6
- Anirud: Wed 1-2 and Thur 4 - 5
- Tanmay: Mon 4 - 5 and Fri 4 - 5
I am away 1 Feb, 3 Feb. These videos are what I would have said. Watch them
I am away 8 Mar, 10 Mar. These videos are what I would have said. Watch them
- I'm travelling 23, 24 Jan, so you won't find me at OH then:
or swing by my office (3310 Siebel) and see if I'm busy
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.
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 Notes, D.A. Forsyth, (approximate 9'th draft)
this course (which is now right, I believe)
I'm a video star! (or at least, I have been filmed)
Probability and Statistics for Computer Scientists, D.A. Forsyth, (approximate 12'th draft)
Notes I made in class:
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
- 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.
- Homework 1, due 23h59 30 Jan 2017
- Homework 2, due 23h59 6 Feb 2017
- Homework 3, due 23h59 20 Feb
- Homework 4, due 23h59 6 Mar 2017
- Homework 5, due 23h59 20 Mar 2017
- Homework 6, due 23h59 10 Apr 2017 Warning! lots of time available for this one, because history shows it's hard. Notice! Figures now on page
- Homework 7, due 23h59 17 Apr 2017 Notice! This is much easier than it looks.
- Homework 8, due 23h59
8 May 2017 This is the take home final for
- Homework 9
(optional; remission of sins), due 23h59 8 May 2017 This is
the remission of sins homework for the course.