Syllabus

• Prerequisites
• Class Times & Locations
• Grades

Prerequisites

Mandatory: Linear Algebra, Basic Probability Theory. Recommended: Signal Processing, Machine Learning.

It is imperative to your success in this course to have a solid grasp on linear algebra and probability. At the beginning of the semester, we will have two review lectures on linear algebra. Instead of teaching the usual topics you would normally cover in a linear algebra class, we will focus on interesting applications of familiar mechanics to topics such as signal separation and music transcription. We will also highlight several topics in probability and information theory that will be important later on, specifically expectations and entropy.

While it is not necessary to have a deep understanding of specific machine learning algorithms, it is always welcome. In a similar vein, this class will deal primarily with machine learning as it applies to signal processing. Therefore, any experience you have with signal processing is welcomed with a smile.

Class Times & Locations

• Meetings TBD Published with the university timetable.
• Location TBD Room assignment pending.
• Zoom TBD Used if a lecture has to move online.

This course will be taught in person. In the event that an instructor is unable to deliver a lecture in person, we will broadcast that lecture over Zoom or, in extreme situations, expect you to view pre-recorded lectures from prior semesters. You will be notified through Piazza should any of these eventualities arise, and the Zoom details will be posted here and on Canvas.

Website: The main course website is online at:

https://cmu-mlsp.github.io/mlspcourse/

Piazza: We will be using Piazza for class communication and announcements. The system is highly catered to getting you help fast and efficiently from classmates and instructors. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza. You can post privately to the instructor and TAs through the Piazza website. The Fall 2026 Piazza space is TBD and will be linked from the home page once it is open.

Canvas: Students submit assignments through Canvas, which is also used for grading and for any request for re-grading. The Fall 2026 Canvas course is TBD and will be available to registered students.

Grades

• Homeworks 50%
• Mini quizzes 24%
• Group project 25%
• Class participation 1%

We will have weekly quizzes that are released on Saturday 12:00 a.m. EST/EDT and due on Sunday night 11:59 p.m. EST/EDT of the same weekend, so 48 hours in total. They are meant to test your knowledge of the previous week’s material as well as provide you guidance on what you should be studying for the upcoming week. Ten multiple-choice questions will be included in each quiz.

We are currently planning to have four (maybe five) homework assignments, released at various points across the semester. The homeworks have been carefully designed as mini projects to ensure that you not only understand the general use case of a technique (e.g., expectation maximization), but can also apply it creatively to an interesting problem (e.g., deblurring an image). You will not catch up if you slack on any of them. So, please start early and feel free to ask for help :-)

Finally, the project is an opportunity for you and your peers to utilize the variety of techniques we cover throughout the semester to solve a compelling issue in a novel way. Topics will be suggested early in the course, and each group should be well-prepared for a video presentation at the end of the semester. Project deadlines and the presentation date are TBD and will be posted on the schedule. The final evaluation is partly done by peer grading.

Attendance is measured by responses to in-class polls. Alternately, viewership of Panopto videos for Kigali students.