teaching

MACHINE LEARNING, Northwestern University EECS 349 Fall 2017

Course Description

Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian learning, decision trees, Support Vector Machines, Reinforcement Learning, Markov models and neural networks.

Time & Place

Lecture: Monday, Wedensday 3:00PM - 4:20PM Tech L211

Recitation Fri 3:00PM - 3:50PM Tech L221 (Kim), Tech L251 (Pishdadian), Tech L168 (EECS Doctoral)

Instructors & Office Hours

Prof. Bryan Pardo Office Hours: Ford Building, Room 3-323, Mon 4:30 - 5:30pm

Bongjun Kim Office Hours: Ford Building, Room 3.317 (West Lounge), Tue 3:30pm-5:30pm

Fatemeh Pishdadian Office Hours: Ford Building, Room 3.317 (West Lounge), Fri 1:00 - 3:00pm

Policies

Grading: You can earn 110 points. Every test & assignment is worth 10 points. You’re graded on a basis of 100 points. In other words… 93-100 is an A, 90 - 92 is an A-, 87-89 is a B+, 83-86 is a B, 80-82 is a B-…and so on.

Extra Credit: The final homework is an extra credit assignment. No other extra credit will be assigned.

Late Policy: Assignments are due on Canvas by 11:59pm on the due date. Canvas is the only way assignments are accepted. Late assignments are docked 2 points per day, starting IMMEDIATELY. For example, an assignment handed in at 12:00am the next day has 2 points removed. An assignment that is 3 days late will have 6 points removed from the final grade.

Cheating & Academic Dishonesty: Do your own work. Academic dishonesty will be dealt with as laid out in the student handbook. Penalties include failing the class and can be more severe than that. If you have a question about whether something may be considered cheating, ask, prior to submitting your work.

Attendance is not graded.

Announcements and discussions will take place on the course Piazza page. You can sign up for the page here.

Course Calendar

Week	Date	Topic	Assigned	Due	Points
1	Wed Sep 20	Decision Trees	HW 1: Decision trees
1	Fri Sep 22	Python 2.7
2	Mon Sep 25	Measuring Distance
2	Wed Sep 27	Nearest Neighbor Class
2	Fri Sep 29	TBD	HW 2: KNNs	HW 1	10
3	Mon Oct 2	Linear regression
3	Wed Oct 4	Linear discriminants
3	Fri Oct 6	TBD	HW 3: Regression	HW 2	10
4	Mon Oct 9	Support Vector Machines
4	Wed Oct 11	Support Vector Machines
4	Fri Oct 13	Midterm preparation		HW 3	10
5	Mon Oct 16	MIDTERM		MIDTERM	10
5	Wed Oct 18	Collaborative Filtering
5	Fri Oct 20	NO CLASS	HW 4: Collaborative Filters
6	Mon Oct 23	Naive Bayesian Classifiers
6	Wed Oct 25	Experimental Validation
6	Fri Oct 27	TBD	HW 5: Naive Bayes	HW 4	10
7	Mon Oct 30	Expectation Maximization
7	Wed Nov 1	Gaussian Mixture Models
7	Fri Nov 3	TBD	HW 6: GMMs	HW 5	10
8	Mon Nov 6	Reinforcement Learning
8	Wed Nov 8	Reinforcement Learning
8	Fri Nov 10	scikit-learn	HW 7: RL	HW 6	10
9	Mon Nov 13	Neural Networks
9	Wed Nov 15	Neural Networks
9	Fri Nov 17	tensorflow	HW 8: Neural Networks	HW 7	10
10	Mon Nov 20	Neural Networks
10	Wed Nov 22	Neural Networks
10	Fri Nov 24	NO CLASS	HW 9: Extra Credit
11	Mon Nov 27	Boosting		HW 8	10
11	Wed Nov 29	Active Learning
11	Fri Dec 1	Final Preparation
12	Mon Dec 4			HW 9	10
12	Fri Dec 8	Final exam (3-5pm)			10