Instructor: Doug Downey
Email: ddowney <at> eecs <dot> northwestern <dot> edu
Teaching Assistants:
Dave Demeter
Zheng Yuan
Xutong Chen
Peer Mentors:
Chiu Yin Cheung
Jared Fernandez
Agam Gupta
Daniel Knight
Vickie Li
Brian Margolis
Avi Vaid
Office Hours:
Monday, 3PM-4PM, Ford 3-340 (Professor)
Wednesday, 11AM-noon, Ford SB-350 (TAs and Peer Mentors)
Thursday, 11AM-noon, Ford 3-340 (TAs and Peer Mentors)
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 April 18 | 15 pts |
| Problem Set 2 | Due 11:59PM April 25 | 5 pts |
| Problem Set 3 | Due 11:59PM May 24 | 10 pts |
| Problem Set 4 | Due 11:59PM June 8 | 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 Friday, May 4 | 5 pts |
| Proposals Peer Review | Due 11:59PM Friday, May 11 | 5 pts |
| Status Report (1-2 pg) | Due 11:59PM Monday, May 21 | 5 pts |
| Status Peer Review | Due 11:59PM Wednesday, May 30 | 5 pts |
| Project Web page | Due 11:59PM Tuesday, June 12 (small bonus for turning in June 10) | 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 April 2 |
T: Introduction W: Decision Trees F: No class |
| Week of April 9 |
M: Decision Trees (cont.) W: Decision Trees (cont.), Instance-based Learning F: Instance-based Learning (cont.) |
| Week of April 16 |
M: Instance-based Learning (cont.) W: Recommenders, Distance Measures F: Linear Regression |
| Week of April 23 |
M: Greedy Local Search, Gradient Descent, Optimization W: Project Guidelines and Suggestions, Exam Review F: Exam 1 |
| Week of April 30 |
M: Genetic Algorithms W: Neural Networks F: Neural Networks (cont.) |
| Week of May 7 |
M: Deep Learning, Recurrent Neural Networks and TensorFlow W: RNNs (cont.) F: No class |
| Week of May 14 |
M: RNNs (cont.) W: Basics of Probability for Machine Learning F: Statistical Estimation |
| Week of May 21 |
M: Naive Bayes Classifiers Logistic Regression W: Unsupervised Learning F: Ensemble Methods |
| Week of May 28 |
M: No class (Memorial Day) W: Bias in Machine Learners, exam review F: Exam 2 |
| Week of May 30 |
M: Computational Learning Theory and Evaluating Hypotheses W: Support Vector Machines F: Reinforcement Learning, MDPs |