CSE 446 - Machine Learning

University of Washington Department of Computer Science & Engineering

CSE 446 - Machine Learning - Spring 2015
MWF 1:30-2:20pm in Mueller Hall 153

Instructor: Pedro Domingos (pedrod at cs)
Office hours: Fridays 2:30-3:20pm
Office: Allen Center 648 TA: Harley Montgomery (wmonty at cs)
Office hours: Wednesdays 11:00-11:50am
Office: Allen Center 220 TA: Maxim Grechkin (grechkin at cs)
Office hours: Wednesdays 2:30-3:20pm
Office: Allen Center 218 TA: Akshay Srinivasan (akshays at cs)
Office hours: Mondays 2:30-3:20pm
Office: Allen Center 220

Schedule

Week	Dates	Topics & Lecture Notes	Readings
1	March 30	Introduction (ppt, pdf), inductive learning (ppt, pdf)	Mitchell, Ch. 1; Duda, Ch. 1
2	April 6	Decision trees (ppt, pdf)	Mitchell, Ch. 3; Duda, Ch. 8
3	April 13	Rule induction (ppt, pdf)	Mitchell, Ch. 10; Duda, Ch. 8
4	April 20	Instance-based learning (ppt, pdf)	Mitchell, Ch. 8; Duda, Ch. 4
5	April 27	Bayesian learning (ppt, pdf)	Mitchell, Ch. 6; Duda, Ch. 2 & 3
6	May 4	Neural networks (ppt, pdf)	Mitchell, Ch. 4; Duda, Ch. 6
7	May 11	Model ensembles (ppt, pdf)	Duda, Ch. 9
8	May 18	Learning theory (ppt, pdf)	Mitchell, Ch. 7; Duda, Ch. 9
9	May 25	Support vector machines (ppt, pdf)	Duda, Ch. 5
10	June 1	Clustering and dimensionality reduction (ppt, pdf)	Duda, Ch. 10

Online Lectures

(Click on "Preview" to see the videos, and on the slides to make them larger.)

Textbooks

R. Duda, P. Hart & D. Stork, Pattern Classification (2nd ed.), Wiley, 2001 (required).
Tom Mitchell, Machine Learning, McGraw-Hill, 1997 (required).

Papers

P. Domingos, A Few Useful Things to Know about Machine Learning. Communications of the ACM, 55 (10), 78-87, 2012.
P. Domingos, A Unified Bias-Variance Decomposition and its Applications. Proc. 17th ICML, 231-238, 2000.

Assignments

There will be four assignments handed out on weeks 2, 4, 6, and 8; they are due two weeks later. Each assignment is worth 25% of the final grade. Submit online here.

Assignment 1 (tex, data/source): Character Recognition with Decision Trees, Rule Induction. Due April 24 at 1:30pm.
Assignment 2: Collaborative Filtering, Bayesian Networks. CHANGED: Due May 11 at 1:30pm.
Assignment 3: Naive Bayes, Neural Nets, Ensemble Methods. Due May 22, 2015 at 1:30pm. Main dataset for Naive Bayes | Original dataset for Extra Credit | Indices of emails used in the Main Dataset
Assignment 4: Learning Theory and SVMs. Due June 5, 2015 at 1:30pm. Original Dataset | LIBSVM Dataset | Reference Implementation | LIBSVM Download/Documentation

Course Administration and Policies

The course mailing list is cse446a_sp15@u.washington.edu (automatically populated).
Discuss course material on the forum.
Assignments will be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
Assignments may be handed in up to a week late, at a penalty of 10% of the maximum grade per day.
Comments can be sent to the instructor or TAs using this anonymous feedback form.

Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX