Machine Learning (ECE), EECS 553, Winter 2026

The goal of machine learning is to develop computer algorithms that can learn from data or past experience to make accurate, useful, and fair predictions on new unseen data. The capabilities and successes of machine learning have grown tremendously over the last few decades, and extremely rapidly over the past few years. This has had major impacts in many real-world applications, yet other applications have seen little to no progress. This course will give a graduate-level introduction of machine learning and provide mathematical and statistical foundations of machine learning, mathematical derivation and implementation of the algorithms, and discussion of their applications. On assignments, students will work through mathematical derivations applying principles learned in class, implement machine learning algorithms in Python, and apply those algorithms to data sets spanning a variety of applications. In small groups, students will also participate in a machine learning reproducibility challenge, which involves reproducing the results and assessing the conclusions of a paper published in a major machine learning conference.

Instructor: Professor Laura Balzano,
Course time: Tuesday/Thursday 12-1:30pm
Course location: TBD. All lectures will be recorded.
Office hours: TBD
GSI(s): TBD
Textbook: None required, but there are several useful references that will be listed on Canvas.
Syllabus draft: download here

FAQ:

What is the difference between EECS 445, 453, 545 and 553?

Starting in Fall 2022, EECS 453/553 are offered by the ECE division. EECS 445/545 are offered by the CSE division. Both 545 and 553 will assume familiarity with probability and linear algebra. EECS 553 specifies experience with graduate coursework in linear algebra and probability. The primary difference between the courses is a matter of emphasis. While both courses will cover fundamental principles, mathematical derivations, and applications to real-world examples, 445/545 will place relatively more emphasis on software implementation and application to real-world data, whereas 453/553 will place relatively more emphasis on mathematical derivations and principles. Both 500-level courses will serve students seeking to do research in machine learning.

I would like to get on the waitlist, but right now it is limited to ECE students. What do I do?

Go to the ECE override system and enter your request.

Can I take the course even if I haven't had graduate coursework in probability and linear algebra?

I discourage students who have not taken probability and linear algebra courses at 500 level or above from taking EECS 553. I encourage you to wait for an opportunity to take EECS 453 - Principles of Machine Learning, or take the pre-requisite courses and then take 553 in future.

Will lectures be recorded?

Yes, all lectures will be recorded using the lecture capture system. Most classes will not be synchronous on zoom, but any class on zoom will be recorded as well.

Can I take 553 along with another class/lab whose classtime overlaps?

No.

There are a lot of people on the waitlist; will the class size be increased or new sections added?

There are always a lot of people on the waitlist for 545/553. The department/divisions do their best to offer as many sections as possible, but any given semester we may not have capacity. This semester no new sections will be added. If more GSIs are available and the classroom can be changed to a larger one, we may be able to increase the class size.

I am on the waitlist and would like to get an override, what do I do?

Fill out the override request form that is linked in an email you will receive.

I received an override, what do I do?

Follow the instructions in the email promptly. See this video for instructions on how to use an override.

I am in spot N on the waitlist, will I get in?

I want everyone who thinks they have the prerequisite knowledge for this class to be able to take this class. Now that the department offers so many ML classes, it's never been an issue. SO MANY people drop in the first weeks. So please assume you will get in. This also means I expect you to do all the work from day 1, no exceptions, so please do that and don't ask for extensions. If you want to use your free dropped score early on, that's fine too.