CS446/ECE449: Machine Learning (Spring 2021)

Course Information

The goal of Machine Learning is to find structure in 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, (5) Reinforcement Learning by Richard Sutton and Andrew Barto, (6) Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David

Course Deliverables:
(1) Homework due at noon, see below for dates (no late submission accepted)
(2) Midterm
(3) Final

Grading:

3 credit: 60% homework (drop 1 homework), 20% midterm, 20% final
4 credit: 60% homework (drop 0 homework), 20% midterm, 20% final

Grading policy is subject to change.

TA Hours:
Time: Monday 5pm, Monday 8pm, Wednesday 4:30pm, Friday 10am, Friday 11am on campuswire.

Final Exam: TBD.

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Instructor & TAs

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