Instructor: Doug Downey
Email: ddowney <at> eecs <dot> northwestern <dot> edu
Teaching Assistants:
Dave Demeter
Zheng Yuan
Peer Mentors:
Will Lundgren
Da Yeon Hwang
Brian Myer Margolis
Nicolas Finkelstein
Office Hours:
(professor) Monday, 4PM-5PM, Mudd 3001
(TAs and Peer Mentors) Wednesday, 11AM-noon, Mudd 3538
(TAs and Peer Mentors) Thursday, 1-2PM, Mudd 3538
Contacting the TAs: Please use the following e-mail address to reach all TAs at once: eecs349northwestern <at> gmail <dot> com
Four homework assignments make up 40 points of the grade, a course project makes up 40 points, and two in-class exams comprise 20 points for a total of 100 points. Grades are assigned using the standard scale (given in the "introduction" lecture notes), so 93-100 points is an A, 90-93 points is an A-, etc.
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. Due dates subject to change:
| Problem Set 1 | Due 11:59PM October 17 | 15 pts |
| Problem Set 2 | Due 11:59PM Nov 1 | 5 pts |
| Problem Set 3 | Due 11:59PM Nov 28 | 10 pts |
| Problem Set 4 | Due 11:59PM Nov 28 | 10 pts |
The Web pages for the completed course projects from 2008, 2010, 2014, 2015, 2016 and and other years are available for perusal.
Deadlines:
| Proposal (1 pg) | Due 11:59PM Monday, Nov 5 | 5 pts |
| Proposals Peer Review | Due 11:59PM Tuesday, Nov 13 | 5 pts |
| Status Report (1-2 pg) | Due 11:59PM Monday, Nov 19 | 5 pts |
| Status Peer Review | Due 11:59PM Wednesday, Nov 28 | 5 pts |
| Project Web page | Due 11:59PM Tuesday, December 11 | 20 pts |
You can use any learning algorithm libraries or packages that you like for your final project. You don't have to code up your own algorithms, but you can do so if you want to make that a focus of your project (implementing algorithms on your own will probably mean you spend less time on data acquisition and experimentation). See links to some general ML and neural net packages below.
General ML packages:
Neural Net packages:
| Week of Sept 24 | F: Introduction |
| Week of Oct 1 |
M: Decision Trees W: Decision Trees (cont.) F: Decision Trees (cont.), |
| Week of Oct 8 |
M: Instance-based Learning W: Instance-based learning (cont.) F: Instance-based learning (cont.) |
| Week of Oct 15 |
M: Distance Measures W: Recommenders (briefly), Linear Regression F: Local Search, Exam Review |
| Week of Oct 22 |
M: Exam 1 W: Gradient Descent, Optimization Project Guidelines and Suggestions F: Genetic Algorithms, Exam Return |
| Week of Oct 29 |
M: Basics of probability for ML W: Statistical Estimation F: Naive Bayes Classifiers |
| Week of Nov 5 |
M: Naive Bayes Classifiers W: Logistic Regression F: Neural Networks |
| Week of Nov 12 |
M: Neural networks (cont.) W: Deep Learning F: Ensemble Methods |
| Week of Nov 19 |
M: Unsupervised Learning W: Computational Learning Theory and Evaluating Hypotheses F: No class (Thanksgiving break) |
| Week of Nov 26 |
M: Theory (cont.) W: Bias in ML, exam review F: Exam 2 |
| Week of Dec 3 |
M: No class W: Support Vector Machines F: Reinforcement Learning, MDPs |