CS446/ECE449: Machine Learning (Fall 2025)

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) Pattern Recognition and Machine Learning by Christopher Bishop, (4) The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie.

Course Deliverables:
(1) Homework, 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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