CS446/ECE449: Machine Learning (Fall 2023)

Course Information

The goal of Machine Learning is to find structure in data. In this course we will cover three main areas, (1) supervised learning, (2) unsupervised learning, and (3) reinforcement learning models. In particular we will cover the following: perceptron, decision trees, Naive Bayes, Gaussian Bayes, 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) Machine Learning, Tom Mitchell, (3) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, (4) Pattern Recognition and Machine Learning by Christopher Bishop, (5) Graphical Models by Nir Friedman and Daphne Koller, and (6) Reinforcement Learning by Richard Sutton and Andrew Barto, (7) Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David.

Course Deliverables:
(1) Homework due at noon, see below for dates
(2) Midterm
(3) Final

Grading:
3 credit: Homework 60% (drop 1 homework), Midterm 20%, Final 20%
4 credit: Homework 60% (drop 0 homework), Midterm 20%, Final 20%

Grading policy is subject to change.

Late Policy: 3 late days in total.

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Instructor & TAs

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