Special Topics in Theory: Analytic Methods in Theoretical Computer Science
Course ID 15759
Description This course surveys analytic tools that are applicable within several areas of modern theoretical computer science, including pseudorandomness, learning theory, algorithms, and complexity.. Topics may include additive combinatorics and Fourier-analytic methods, Boolean function analysis, concentration and high-dimensional probability, etc.
Key Topics
Fourier-analytic methods, additive combinatorics, Boolean functions, high-dimensional probability
Required Background Knowledge
Strong math skills, linear algebra and calculus
Course Relevance
Master's or doctoral student interested in theory, relevant to students in Tepper/Math/ACO more broadly probably
Course Goals
Ability to recognize and solve problems that involve analytic methods
Learning Resources
none
Assessment Structure
80% HW, 20% Project
Extra Time Commitment
n/a
Course Link
https://yangpliu.github.io/opt