CSE 546 - Machine Learning

University of Washington Department of Computer Science & Engineering

CSE 546 - Machine Learning - Winter 2012
Mon, Wed 1:30-2:50 in EEB 003

CSE Home

About Us

Contact Info

Instructor: Luke Zettlemoyer (lsz at cs dot washington dot edu)
Office hours: Tuesdays 11:00-12:00, CSE 658 TA: Lydia Chilton (hmslydia at cs dot washington dot edu)
Office hours: Mondays 3-4, CSE 5th floor lounge *NOTE DAY CHANGE*

Schedule [subject to change]

Week	Dates	Topics & Lecture Notes	Readings
1	Jan 4	Introduction	Bishop Ch. 1 (optional; mostly review)
2	Jan 9, 11	Decision Trees	Mitchell, Ch. 3; Duda, Ch. 8; ID3 Paper
3	Jan 18	Ugh, snow...	Snow, yeah!
4	Jan 23, 25	Point Estimation; Linear Regression	Bishop 2.1-2.3.5;3.1-3.2
5	Jan 30, Feb 1	Naive Bayes; Logistic Regression; Perceptron	Naive Bayes and Logistic Regression; Perceptron proofs(optional)
6	Feb 6, 8	Boosting , Clustering, EM	Bishop 14.3; 9.1-9.4; Boosting Overview Paper
7	Feb 13, 15	EM (cont.); Instance Based Learning; Dimensionality Reduction	Bishop 2.5; 12.1
8	Feb 22	PCA (see Dim Red slides), Support Vector Machine (SVMs)	Bishop 7.1
9	Feb 27, 29	SVM Dual and Kernels (see SVM slides); Learning Theory	Bishop 6.1-6.2
10	Mar 5, 7	Learning Thery (continues); Neural Networks	Bishop 5.1-5.3

Textbooks

Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
Optional: Tom Mitchell, Machine Learning, McGraw-Hill, 1997.
Optional: R. Duda, P. Hart & D. Stork, Pattern Classification (2nd ed.), Wiley, 2001.

Contact

Please feel free to email the course staff, addresses above, and come to office hours. Let us know if you need to meet outside of the scheduled hours, we will do our best to accomodate.
We also have a GoPost discussion board. Please consider posting your questions there; everyone will benefit. We also encourage you to try to answer questions, which will count towards class participation. We will monitor daily and contribute as long as the boards are being used.
Grades: Assignment grades are posted in the online CSE 546 Gradebook. Please let us know if you see any errors.

Homeworks

We will have 3-4 homework assignments, which will be listed below as they are assigned.

Assignment 1: Decision Trees [pdf] [support code] (Due Wed Jan 25, 5pm)
Assignment 2: Classifiers: Naive Bayes, Perceptron, Logistic Regression [link] [support code] (Due Wed Feb 8, 5pm)
Assignment 3: Boosting, Clustering, EM [link] (Due Fri Feb 17, 5pm)

Please submit your assignments to the online DropBox.

Final Mini-project

A final mini-project will completed during the last weeks of the term. See the full description. Please submit your code and writeup to the online DropBox.

Grading

The final grade will consist of homeworks (40%), a mid-term exam (25%), a final mini-project (30%), and course participation (5%).

Course Administration and Policies

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 TA 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