COL863: Special Topics in Theoretical Computer Science
Topic: Mathematics of Data Science

II semester: 2020-21

Amitabha Bagchi

Class Timings: M Th 3:30PM on Teams.

Course objectives

At the end of the course the student is expected to develop a working familiarity with the mathematical foundations of most of the techniques used in data science, machine learning and AI.

Background required: Basics of Probability, Graph Theory, and Linear Algebra.


Geometry of High-dimensional space including dimensionality reduction; Singular Value Decomposition and applications; Random walks and Markov Chains; Sketching and sampling; Clustering.


Course calendar

Refresher texts


Audit Pass criterion. At least 8/30 in each minor and at least 35/100 overall. Plagiarism will lead to automatic audit fail. In case you have missed a minor and still wish to audit the class you can do so if you make up for your missed minor with 5/10 in the term paper.

Plagiarism. Copying text from any source (including but not limited to internet, video, book, another person's paper) constitutes plagiarism. Since this is an elective 800-level course, a very high standard of honesty is expected. Violations will be treated accordingly.

Amitabha Bagchi