Algorithms for Data Science
CS 2243, Fall 2024

Course Details

This is a graduate topics class on algorithmic challenges in modern machine learning and data science. We will touch upon a number of domains (generative modeling, deep learning theory, robust statistics, Bayesian inference) and frameworks for algorithm design (spectral/tensor methods, gradient descent, message passing, MCMC, diffusions), focusing on provable guarantees. The theory draws upon a range of techniques from stochastic calculus, harmonic analysis, statistical physics, algebra, and beyond. We will also explore the myriad modeling challenges in building this theory and prominent paradigms (average-case complexity, smoothed complexity, oracles) for going beyond traditional worst-case analysis.

Time/Location: MW 2:15-3:30, SEC 1.413

Instructor: Sitan Chen (sitan@seas.harvard.edu)
Office hours: SEC 3.325, Monday 5-6

Teaching Fellows:

  • Weiyuan Gong (wgong@g.harvard.edu), Office hours: TBD
  • Marvin Li (marvinli@college.harvard.edu), Office hours: TBD
Pset office hour: TBD

Canvas (for lecture recordings only)

Gradescope

Ed (discussion)

Course Policies: See syllabus for detailed overview.


Announcements


Assignments

Assignments will be posted below and in the course Overleaf when they become available.

  • Pset 0 (due 09/03 at 4:59pm): PDF


Miscellaneous Materials


Lectures

Date Topic Lecture Notes Resources
Sep 4 Logistics, vignette: diffraction limit and learning theory instructor notes, slides