Algorithms for Data Science
CS 224, Fall 2023

Course Details

This is a graduate-level topics class on algorithmic challenges arising in modern machine learning and data science more broadly. The course will touch upon a number of well-studied problems (generative modeling, deep learning theory, adversarial robustness, inverse problems, inference) and frameworks for algorithm design (gradient descent, spectral methods, tensor/moment methods, message passing, convex programming hierarchies, Markov chains). As we will see, proving rigorous guarantees for these problems often draws upon a wide range of techniques from stochastic calculus, harmonic analysis, random matrix theory, algebra, and statistical physics. We will also explore the myriad modeling challenges that go into building theory for ML and discuss prominent paradigms (semi-random models, smoothed analysis, oracles) for going beyond traditional worst-case analysis.

Time/Location: MW 2:15-3:30, SEC LL2-224

Instructor: Sitan Chen (
Office hours: SEC 3.325, Thursday 4-5

Teaching Fellows:

  • David Brewster (, Office hours: Maxwell-Dworkin 123, Tuesday 5-6
  • Depen Morwani (, Office hours: SEC 1.404, Wednesday 11-12
  • Rosie Zhao (, Office hours: SEC 2.330, Monday 11-12
Pset office hour: SEC 3.314, Thursday 4-6 on weeks with a pset deadline

Canvas (for lecture recordings and announcements)


Ed (discussion)

Course Policies: See syllabus for detailed overview.


  • Pset 1 is out, see below.

  • See Canvas for link to sign up for pset office hour preferences

  • Notifications of course placement will be sent out Thursday afternoon, Aug 31.

  • Pset 0 and course application form due August 29, 11:59pm


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

Miscellaneous Materials


Date Topic Lecture Notes Resources
Sep 6 Logistics, vignette: diffraction limit and learning theory instructor notes, slides
Sep 11 Tensors I: Jennrich's algorithm, applications (super-resolution, Gaussian mixtures, independent component analysis) instructor notes, slides, scribe notes
Sep 13 Tensors II: Iterative methods (gradient descent, tensor power method, alternating least squares) instructor notes, slides, scribe notes
Sep 18 Tensors III: overcomplete tensor decomposition via smoothed analysis instructor notes, slides
Sep 20 Sum-of-squares I: pseudo-distributions, application to robust regression instructor notes, slides