CS446/ECE449: Machine Learning (Spring 2020)

Course Information

The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas, (1) discriminative models, (2) generative models, and (3) reinforcement learning models. In particular we will cover the following: linear regression, logistic regression, support vector machines, deep nets, structured methods, learning theory, kMeans, Gaussian mixtures, expectation maximization, VAEs, GANs, Markov decision processes, Q-learning and Reinforce.

Pre-requisites: Probability, linear algebra, and proficiency in Python.

Recommended Text: (1) Machine Learning: A Probabilistic Perspective by Kevin Murphy, (2) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, (3) Pattern Recognition and Machine Learning by Christopher Bishop, (4) Graphical Models by Nir Friedman and Daphne Koller, and (5) Reinforcement Learning by Richard Sutton and Andrew Barto.

Grading:

3 credit: Homework 33%, Midterm 33%, Final 33%

4 credit: Homework 16.6%, Scribe 16.6%, Midterm 33%, Final 33%

The lowest homework grade will be dropped (the scribe cannot be dropped) and we will compute the average score of the remaining 9 assignments.
Grading policy is subject to change.

TA Hours:
Time: Tuesdays/Thursdays: 5-6:30pm (2 TAs) on days before homework deadlines and 5-6pm (1 TA) otherwise.
Room: ECEB 2015.
No TA hours on February 27 and April 9.

Late Policy: No late submission will be accepted after the due date.

Midterm: March 12, 12:30pm - 13:45. Room: TBD.
Final Exam: May 14, 7-10pm. Rooms: 1002 ECEB, 1013 ECEB, 3013 ECEB, 3017 ECEB.

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Instructor & TAs

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