CS 289A: Machine Learning (Spring 2021)


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. Sign up your group for a ten-minute meeting slot with one of them on this Google spreadsheet before 11:59 PM on April 4. The Project TAs, and their areas of expertise, are:

Deirdre Quillen dequillen@gmail.com: using machine learning to predict wildfire spread and risk, and to forecast other weather variables; reinforcement learning; robotic manipulation.
Yaodong Yu yaodong_yu@berkeley.edu: robust machine learning, optimization for machine learning, empirical/theoretical understanding of deep learning.
Zhuang Liu zhuangl@berkeley.edu: neural networks and computer vision related problems.



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

You are welcome and encouraged to design a project that is related to your research outside this course. However, please be honorable and don't suggest a project that you've already 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.


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: