Instructor: Doug Downey
Office Hours: 3:00-4:00PM Monday EXCEPT April 4 when instead 5PM-6PM. Ford 3-345
Email: ddowney <at> eecs <dot> northwestern <dot> edu
Teaching Assistants:
Mohammed Alam ("Rony")
Chen Liang
Nishant Subramani
Shengxin Zha
Jacob Samson
Hosung Kwon
Office Hours: Wednesday 11-12AM, Ford 3-210 Monday 11-12AM, Tech D130
Peer Mentor group office hours: Wednesday 2:00-3:20, Ford 3-340
Contacting the TAs: Please use the following e-mail address to reach all TAs at once: eecs349northwestern <at> gmail <dot> com
Homework will be submitted via Canvas. Details on the specific files to include are given in each homework assignment.
Late peer review assignments are penalized 33% per day. All other late assignments are penalized by 10% a day, and will NOT BE ACCEPTED more than one week after the original deadline.
Problem Set 1 | Due 11:59PM Tuesday, April 12 | 10 pts |
Problem Set 2 | Due 11:59PM Monday, May 9 | 15 pts |
Problem Set 3 | Due 11:59PM Tuesday. May 31 | 10 pts |
Problem Set 4 | Due 11:59PM Thursday, June 2 | 10 pts |
Deadlines:
Proposal (1 pg) | Due 11:59PM Thursday, April 14 | 5 pts |
Proposals Peer Review | Due 11:59PM Wednesday, April 20 | 5 pts |
Status Report (1-2 pg) | Due 11:59PM Monday, May 16 | 5 pts |
Status Peer Review | Due 11:59PM Monday, May 23 | 5 pts |
Project Web page | Due 11:59PM Wednesday, June 8 | 30 pts |
|
Week of March 28 |
Alpaydin Ch. 1, 2 (skip 2.2, 2.3), 9 Optional: When to Hold Out for a Lower Airfare Optional: Thinking Big about the Industrial Internet of Things |
Week of April 4 |
Alpaydin 8, 19.5, 19.6 |
Week of April 11 |
Alpaydin 5.4, 10.6 Brief LSH tutorial Finding Similar Items |
Week of April 18 |
Alpaydin Ch. 11 |
Week of April 25 | Optional AlphaGo paper |
Week of May 2 | Alpaydin Ch. 3, 16 |
Week of May 9 |
Alpaydin Ch. 7 |
Week of May 16 | None |
Week of May 23 | Alpaydin Ch. 17, 19 |
Week of May 30 |
Recommended: SVM Tutorial Alpaydin Ch. 13 |
Week of March 28 |
T: Introduction W-F: Decision Trees |
Week of April 4 |
M: Decision Trees (cont.) W: Project Guidelines and Suggestions F: Instance-based Learning |
Week of April 11 |
M: Instance-based Learning (cont.), Distance Measures W: Locality-sensitive hashing and MinHash 1 2 F: Greedy Local Search, Optimization |
Week of April 18 |
M: Genetic Algorithms, See: NetLogo W-F: Neural Networks |
Week of April 25 |
M: RNNs and Tensorflow (Chen Liang guest lecture) W: Neural Networks (cont.) See: TextJoiner, Word2Vec Demo F: Deep Learning |
Week of May 2 |
M: Basics of Probability for Machine Learning W: Statistical Estimation F: Bayes Nets |
Week of May 9 |
M: Bayes Nets (cont.) W: Naive Bayes Classifiers F: Logistic Regression |
Week of May 16 |
M-W: Unsupervised Learning, part 2 F: Project Status Reports |
Week of May 23 |
M: Clustering (cont.) W: Ensemble Methods F: Computational Learning Theory and Evaluating Hypotheses |
Week of May 30 |
M: No class (Memorial Day) W: Learning Theory (cont.) F: Support Vector Machines |