DS-GA 1005 - Fall 2021
The aim of this graduate-level course is to describe the mathematical aspects of modeling high-dimensional data, with an emphasis on computational and statistical theoretical questions. Topics include probabilistic graphical models, variational inference, MCMC methods, causal inference, optimal transport, and tools from statistical physics.
Logistics
- Lectures (section 001): Tuesdays at 4.55pm-6.35pm ET - In person, 60FA_150 (60 5th Avenue, room 150)
- Labs: Either one of the two
- Section 002: Mondays at 7pm-7:50pm ET - In person, GCASL_361 (Global Center for Academic & Spiritual Life, 238 Thompson Street, room 361)
- Section 003: Mondays at 4pm-4:50pm ET - In person, SILV_520 (Silver Center for Arts & Science, 100 Washington Square East, room 520)
- Office hours:
- Mondays, 5:00pm-6:45pm ET with Carles (room 204, 60 5th Ave)
- Thursdays 3:00pm-4:00pm with Alberto (room 607, 60 5th Ave)
- Campuswire: link
Instructors
Lecture Instructor: Alberto Bietti ([email protected])
TA: Carles Domingo-Enrich ([email protected])
General information
- Syllabus
- Grading
- Bibliography
- Other courses on Inference
Final Project
Class recordings
- Lectures videos
- Recitations videos