CS 289A: Machine Learning (Spring 2024)
Project

 

20% of final grade. The project should be done in teams of 2–3 students. Please find a partner.

Please discuss your ideas with one of the Project Teaching Assistants before submitting your initial proposal. The Project TAs, and their areas of expertise, are:

Pierre Boyeau, pierreboyeau@berkeley.edu: single-cell RNA modeling using deep generative models.
Charles Dove, charles_dove@berkeley.edu: machine learning, biomedical imaging, photonic design, artificial vision, quantum mechanics, and quantum field theory.
Norman Mu, thenorm@berkeley.edu: deep learning robustness and safety; multimodal visual recognition; large language models/assistants.

Deliverables

Overview

The project theme may be anything related to machine learning techniques discussed in class, including

You are encouraged to design a project that is related to your research outside this course; we really hope that the project will help you make progress in your primary research duties (or a teammate's). However, please be honorable and don't suggest a project that you've already fully completed as part of your research.

Initial proposal

The initial proposal is primarily a proposal, and need not be long. Write a few paragraphs describing what you have decided to do. You may have any number of figures and references.

Video

Final report

Grading criteria

The video and the final report will be graded with 5 criteria.

Project ideas

The ideas in this list fall mainly under the fourth category, practical research. If you prefer to revisit an important paper, simply pick a paper. If you prefer to conduct a literature review, simply pick a machine learning topic that interests you. If you prefer to conduct theoretical research, you'd better already know what you're doing.

Other useful data sources: