Topics in mathematics of data science

Lectures: 2 sessions / week, 1.5 hours / session

Working knowledge of 18.06SC Linear Algebra and 18.05 Introduction to Probability and Statistics is required. Some familiarity with the basics of optimization and algorithms is also recommended.

This is a mostly self-contained research-oriented course designed for undergraduate students (but also extremely welcoming to graduate students) with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data. These often lie in overlaps of two or more of the following: Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Statistics, and / or Operations Research.

The topics covered include:

40% of the grade is based on a handful of homework problem sets (to be handed out roughly bi-weekly). You are welcome to work on the problem sets in groups, but you have to write your own solutions.

60% of the grade is based on a project. The project (which can be done individually or in groups of two) can be a literature review, but I would recommended attempting to do original research, either by trying to make partial progress on (or completely solve!) one of the open problems, or by pursuing another research direction. The project report is due on the last week of classes. A preliminary abstract will be due roughly a month before the project due date and each group is expected to make a 5 minute presentation on class about their project before the due date.

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