Instructor:

Teaching Assistant:

Lecture Schedule:

<aside> <img src="/icons/calendar-month_gray.svg" alt="/icons/calendar-month_gray.svg" width="40px" /> Lectures: T | 6:00 PM - 8.50 PM, Room Atwater Kent 233

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<aside> <img src="/icons/warning_red.svg" alt="/icons/warning_red.svg" width="40px" /> If school is canceled due to a "significant weather event" (e.g., "snow day") or class is canceled due to an unforeseen event, I expect to use our pre-recorded lecture material (see the Lecture Notes section of this web page) to fill-in for any missed lecture.

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Website:

<aside> <img src="/icons/globe_blue.svg" alt="/icons/globe_blue.svg" width="40px" /> https://bashima.notion.site/on-device-deep-learning-s25

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All course materials, problem sets, solutions, announcements, and other valuable tidbits will be available via this website and the CANVAS site. The official site of the course will not have the problem sets and their solutions.

Online Lectures – In this course, all lectures will be recorded and posted online via the CANVAS website after the class. This recordings are distributed for reviewing after the class. This is NOT the replacement of the in-person lectures. Before each class a pre-note will be distributed through CANVAS/website. The students are expected to utilize it to follow the class.

Paper Presentation – This course includes paper discussion sessions where a group of student will present an assigned conference paper from the top conferences in the area. After the presentation, there will be a discussion session, and a student will be designated as a Scribe to write the transcription. The list of papers would be available on this website and on Canvas.

Course Textbook:

• We will not be using any dedicated book.

The materials of this course are inspired by the TinyML and Efficient Deep Learning Computing course taught by Professor Song Han at MIT.

Recommended Background:

• The students should have an introductory undergraduate-level background in machine learning and deep neural networks.

Evaluation:

Projects (35%) -- Teams of up to three students each are expected to complete the projects. The students themselves will propose this project and need to involve the topics taught in the classes. The project proposal also needs to be approved by the instructor. All source codes need to be uploaded to GitHub. Final reports (PDF format only), including links to all the source codes uploaded to GitLab, will be submitted via the CANVAS course website. A descriptive video including a demo for the course youtube channel is required and will hold (5%) of the score. This needs to be uploaded to the provided youtube channel link before evaluation. Failure to do so will deduct points. Note that each student team is expected to work independently. During code evaluation, each student's role will be judged, too.

Project Rubric for On-Device Deep Learning

Project Proposal Guideline

Assignments (30%) -- Three coding assignments (all equally weighted) will be assigned throughout the course. Assignments source codes need to be submitted on Canvas. Full credit for assignment handed in before 5 P.M. on the due date. Assignments handed in late but by 10 a.m. on the day after they are due will be graded for full credit only once. There is no guarantee of any credit for assignments submitted after this time. (Contact Bashima Islam immediately for an exception, preferably before the due date.)