ECE 364: Programming Methods for Machine Learning (Fall 2024)

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.

Grading:

40% Homeworks; 30% each Midterm

Optional Bonus Project, worth up to +5% (you may only complete one), to be completed individually:

Due Date: Saturday, December 21, 11:59pm on Gradescope

Option #1 (Background-Foreground Separation): Project 1 Statement, Project 1 Data, Project 1 Helper Codes

Option #2 (Object Detection): Project 2 Statement, Project 2 Data, Project 2 Helper Codes

Office Hours for Bonus Project (Zoom only, unless requested in-person)

Link: https://illinois.zoom.us/j/89045618539?pwd=zzirsiMKpKIMX7eeJqULz5flYyZgzf.1

Monday: 11am-1pm (Central Time)

Tuesday: 12-2pm

Thursday: 11am-1pm

Friday: 9am-11am

Lectures

The syllabus is subject to minor changes.