CS 58500 Theoretical Computer Science Toolkit, Spring 2026

Course Details

Description: This course covers fundamental techniques and a range of mathematical tools that underlie today’s research in theoretical computer science and are essential knowledge for students pursuing research in theoretical computer science or machine learning theory. The course targets current graduate and undergraduate students interested in pursuing research in these areas. Topics will be chosen from four core areas: 1) Concentration Inequalities and their Applications. 2) Selected Topics in Convex Analysis and Optimization. 3) Foundations of Spectral Methods. 4) Discrete Fourier Analysis. Depending on student interest, additional topics will be chosen. The topics may include applied analysis for learning theory, coding theory, probabilistic proofs, graph algorithms, and sampling.

Time/Location: TTH 3:00 PM - 4:15 PM at HAAS G066

Instructor: Ruizhe Zhang Office hours: by appointment only

Teaching Assistant: Xiuyu Ye Office hours: 10:30 AM - 11:30 AM at DSAI B055

Course policy: See syllabus for detailed overview.

Assignments

Assignments will be posted below and in Brightspace when they become available.

Lectures

The schedule will be updated as we progress through the course.

Date Topic Materials Resources
Jan 13 Introduction [slides]
Jan 15 No lecture
Jan 20 Mathematical Basics
Jan 22 Concentration inequalities (I): Markov inequality and Chebyshev inequality
Jan 27 Concentration inequalities (II): Chernoff bounds
Feb 3 Concentration inequalities (III): McDiarmid’s Inequality
Feb 5 Convex geometry and analysis (I)
Feb 10 Convex geometry and analysis (II)
Feb 12 Convex geometry and analysis (III): Isoperimetric inequalities, localization, and the KLS conjecture
Feb 17 Convex optimization (I): Gradient descent
Feb 19 Convex optimization (II): Polynomial approximation
Feb 24 Spectral methods (I): Basics
Feb 26 Spectral methods (II): Markov chains
Mar 3 Spectral methods (III): Matrix perturbation theory
Mar 5 Matrix concentration inequalities (I)
Mar 10 Matrix concentration inequalities (II)
Mar 12 Boolean function analysis (I): Basics of discrete Fourier analysis
Mar 24 Boolean function analysis (II): Linearity testing
Mar 26 Boolean function analysis (III): Degree, query complexity, and sensitivity
Mar 31 Boolean function analysis (IV): Degree, query complexity, and sensitivity
Apr 2 Boolean function analysis (V): Isoperimetric inequalities over the hypercube
Apr 7 Boolean function analysis (VI): Isoperimetric inequalities over the hypercube
Apr 9 Special topics (I)
Apr 14 Special topics (II)
Apr 16 Special topics (III)
Apr 21 Special topics (IV)
Apr 23 Project presentations
Apr 28 Project presentations
Apr 30 Project presentations

Resources