Machine Learning (EECS 349)

Winter 2014
Electrical Engineering and Computer Science Department
Northwestern University

Class Meets: 2:00PM-2:50PM MWF, Tech LR5

Instructor: Doug Downey
Office Hours: 1:00-2:00PM Monday (or by appt), Ford 3-345
Email: ddowney <at> eecs <dot> northwestern <dot> edu

Teaching Assistants: Chandra Sekhar Bhagavatula, Shengxin Zha and Kathy Lee
Office Hours: Thursday 2PM-3PM, Ford 3-211 and Friday, 3PM-5PM, Tech L440
Email: chandrabhagavatula2011 <at> u <dot> northwestern <dot> edu,   shengxinzha2011 <at> u <dot> northwestern <dot> edu,   kathy <dot> lee <at> eecs <dot> northwestern <dot> edu

Completed Course Projects


Four homework assignments make up 50 points of the grade, a course project makes up 55 additional points, for a total of 105 points. Grades are assigned using the standard scale (given in the "introduction" lecture notes), so 93-105 points is an A, 90-93 points is an A-, etc.


Homework will be submitted via Blackboard. Details on the specific files to include are given in each homework assignment.

Late assignments are penalized by 5% a day, and will NOT BE ACCEPTED more than one week after the original deadline.
Problem Set 1Due 11:59PM Tuesday, Jan 2110 pts
Problem Set 2Due 11:59PM Tuesday, Jan 2820 pts
Problem Set 3Due 11:59PM Tuesday, Feb 1810 pts
Problem Set 4Due 11:59PM Thursday, March 1310 pts

Course Projects

Course projects will be performed by groups of students; any number of students can work together, but larger groups will be expected to complete more substantial projects. Your assignment is to formulate an interesting task for which machine learning can be used, gather training and test data for the task, and then evaluate one or more machine learning algorithms on the data. The final output will include a report Web page and 5-minute video. The videos will be shown to the class on the finals day for the course, March 21. The Web pages for the completed course projects from 2008, 2010, and other years are available for perusal.

Proposal (1 pg)Due 11:59PM Thursday, Feb 610 pts
Status Report (1-2 pg)Due 11:59PM Tuesday, March 410 pts
Project VideoDue 9AM Friday, March 2120 pts
Project Web page (details in link above)Due 9AM Friday, March 2115 pts


Week of January 6 Wired data-mining article
Forbes article on ML popularity
Alpaydin Ch. 1,2; Mitchell Ch. 1,2
Week of Jan 13 Alpaydin Ch. 8,9; Mitchell Ch. 3,8
Week of Jan 20 None
Week of Jan 27 Alpaydin Ch. 10.6; Mitchell Ch. 9
Week of Feb 3 Alpaydin Ch. 11; Mitchell Ch. 4
Week of Feb 10 Alpaydin Ch. 4.2, 16; Mitchell Ch. 6
Recommended: Andrew Moore tutorial on Bayes Nets
Week of Feb 17 Alpaydin Ch. 10
Optional: Modeling Redundancy in Web Information Extraction
Week of Feb 24 Alpaydin Ch. 7.4, 15; Mitchell Ch. 7
Week of Mar 3 Alpaydin Ch. 13
Recommended: SVM Tutorial

Lectures and Schedule (subject to change)

Week of Jan 6 M: No class (NU closed for weather)
W: Introduction
F: Decision Trees
Week of Jan 13M-W: Decision Trees (cont.)
F: Instance-based Learning
Week of Jan 20M: No class (MLK)
W: Instance-based Learning (cont)
F: Distance Measures
Week of Jan 27 M: Project Guidelines and Suggestions
W: Greedy Local Search, Optimization
F: Genetic Algorithms
Week of Feb 3M: Neural Networks
W: Neural networks (cont.)
F: Neural networks (cont.), Basics of Probability for Machine Learning
Week of Feb 10M: Basics of Prob. (cont.)
W: Statistical Estimation
F: Bayes Nets
Week of Feb 17 M: Naive Bayes Classifiers
W: Web Information Extraction
F: Logistic Regression
Week of Feb 24 M: Hidden Markov Models
W: Clustering, EM
F: Clustering, EM (cont.)
Week of March 3 M: Computational Learning Theory and Evaluating Hypotheses
W: Project Status Reports
F: Support Vector Machines
Week of March 10 M: Ensemble Methods
W: Reinforcement Learning
F: Active Learning