daf@uiuc.edu, daf@illinois.edu

15:30 - 16:45 OR 3.30 pm-4.45 pm, in old money

TuTh

1320 Digital Computer Laboratory

**TA's:**

Mariya Vasileva mvasile2@illinois.edu

Sili Hui silihui2@illinois.edu

Daeyun Shin dshin11@illinois.edu

Ayush Jain ajain42@illinois.edu

**Office Hours: **

** Ayush ** Fri - 14h00-16h00 or 2-4 pm, ** location:** in front of 3304

** Daeyun ** Thu - 11h00-13h00 or 11 am-1 pm **location: ** 0207 Siebel/p>

** Mariya ** Wed - 15h00-17h00 or 3 - 5 pm **location:** 0207 Siebel

** Sili ** Thur - 12h00-14h00 or 12 - 2 pm **location:** 0207 Siebel

** DAF ** Mon - 14h00-15h00, Fri - 14h00-15h00

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

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 4'th draft)

- Version of 19 Jan 2016
- Version of 21 Jan 2016
- Version of 26 Jan 2016
- Version of 1 Feb 2016
- Version of 11 Feb 2016
- Version of 16 Feb 2016
- Version of 3 Mar 2016 (Note correction in description of EM for topic models; cleaner regression chapter)
- Version of 15 Mar 2016 (Discussion of bias and variance is now correct, blush; Bayesian regression is comprehensible)
- Version of 28 Mar 2016 (Bayesian regression abandoned; more R code for glmnet, and more sparse examples; much better non-parametric stuff)
- Version of 31 Mar 2016 (minor corrections)
- Version of 7 April 2016 (more on neural networks)
- Version of 12 April 2016 (yet more neural networks)
- Version of 21 April 2016 (residual networks updated; and a bunch of HMM/structure learning stuff)
- Version of 28 April 2016 (a bunch more HMM/structure learning stuff)

Probability and Statistics for Computer Scientists, D.A. Forsyth, (approximate 12'th draft)

- 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 1 Feb 2016
- Homework 2, due 23h59 8 Feb 2016
- Homework 3, due 23h59 22 Feb 2016
- Homework 4, due 23h59 29 Feb 2016
**DUE DATE REVISED: 3 Mar 2016** - Homework 5, due 23h59 7 Mar 2016
**DUE DATE REVISED: 14 Mar 2016** - Homework 6, due 23h59 4 April 2016
**NOTE REVISIONS TO CLARIFY** - Homework 7, due 23h59 11 April 2016
- Optional Homework, due 23h59 2 May 2016
**THIS IS AN OPTIONAL EXTRA ON HW7, BUT CAN BE USED FOR REMISSION OF SINS, etc**## Notice correction that makes above optional homework do-able! (sorry!)

- Optional Homework, due 23h59 2 May 2016
**THIS IS OPTIONAL, BUT CAN BE USED FOR REMISSION OF SINS, etc** - Homework for the four hour people, due 23h59 2 May 2016
**THIS IS FOR PEOPLE WHO ARE TAKING THIS CLASS 4 HOURS** - Homework 8, due 23h59 4 May 2016
**THIS IS THE TAKE-HOME FINAL**

- An R Tutorial, prepared by Karthik Sridharan with revisions by Henry Lin
- An introduction to R
- Resources to help you learn and use R, from UCLA
- R tutorials, from W.B. King, Coastal Carolina University
- R code school, where you can get badges (?!?)