ECE 364: Programming Methods for Machine Learning (Fall 2025)

Course Information

In this course, you will learn how to use auto-differentiation tools like PyTorch, how to leverage them for basic machine learning algorithms (linear regression, logistic regression, deep nets, k-means clustering), and how to extend them with custom methods to fit your needs. Auto-differentiation is one of the most important tools for data analysis and a solid understanding is increasingly important in many disciplines. In contrast to existing courses that focus on algorithmic and theoretical aspects, here we focus on studying material that permits deploying auto-diff tools to your area of interest.

Pre-requisites: Math 257 (Linear Algebra with Computational Applications) or equivalent, basic probability, and proficiency in Python.

Recommended Reference Texts: (1) Pattern Recognition and Machine Learning by Christopher Bishop
(2) Machine Learning: A Probabilistic Perspective by Kevin Murphy
(3) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

Please note that these books are more comprehensive than the material covered in this class.

Course Deliverables:
(1) Homeworks (submission on Gradescope).
(2) Midterm Exams: There will be two midterm exams.
(3) Final Project

Final Project:

More details to come.

Homework and Late Policy:

We will drop the lowest homework grade (1 assignment) for each student. Late submissions will be deducted 20% per day that the assignment is submitted late.

Grading:

25% Homeworks; 25% each Midterm; 25% Final Project

Lectures

Lecture recordings may be found here.

The syllabus is subject to minor changes.