DS-GA 1005 - Fall 2020

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, optimal transport, and tools from statistical physics.

Logistics

• Lectures: Tuesdays at 4.55pm-6.35pm ET - Blended (Online & In-Person [19W4, Room 101]) - Zoom

• Recitations: Either one of the two

  • Mondays at 8.00am-8.50am ET - Online - Zoom
  • Mondays at 4.55pm-5.45pm ET - Blended (Online & In-Person [MEYR 122]) - Zoom

• Campuswire: Link

• Office Hours: Tuesdays at 9:30am-11am ET - Zoom

Instructors

Lecture Instructors:

• Joan Bruna ([email protected])
• Ilias Zadik ([email protected])

TAs:

• Aaron Zweig ([email protected])
• Luca Venturi ([email protected])

General information

• Syllabus
• Grading
• Bibliography
• Courses on Inference we like

Final Project