Schedule

Online Lectures

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.