STA414 Winter 2020: Statistical Methods for Machine Learning II

Overview

The language of probability allows us to coherently and automatically account for uncertainty. This course will teach you how to build, fit, and do inference in probabilistic models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. This course will teach the basic building blocks of these models and the computational tools needed to use them.

When, Where, and Who

Lectures:

  • Section: LEC0101

    • Time: Monday 14:00-17:00
    • Room: EM 001
  • Section: LEC5101

    • Time: Tuesday 19:00-22:00
    • Room: SS 2117

Discussion Forum: Requires Invitation
Instructors: David Duvenaud and Jesse Bettencourt
Instructor Email: sta414prof AT cs DOT toronto DOT edu
Instructor Office Hours:
David
Time: Thursdays 15:15 - 16:45
Room: Pratt 384

Jesse
Time: Wednesdays 13:10-14:00
Room: Bahen 2283

Teaching Assistants: Bo Chen, Jacob Kelly, Lu Yu, Emad Zadegan
TA Email: sta414tas AT cs DOT toronto DOT edu
TA Office Hours:
Teaching Assistants will hold weekly office hours in the Stats Help Centre:
Public Health Sciences Building 155 College St. 3rd floor, room PHS 381

  • Mondays: 9:00 - 10:00
  • Tuesdays: 15:30 - 16:30
  • Thursday: 10:00 - 12:00

Tentative Schedule

  • Assignment 0: 10% (Jan 24)
  • Assignment 1: 13.3% (Feb 7)
  • Midterm: 20% (Feb ~13)
  • Assignment 2: 13.3% (Mar 13)
  • Assignment 3: 13.3% (Apr 3)
  • Final Exam: 30% (TBD)

Course Syllabus

Course Information inclduing Schedule, Contact Information, Links to Material, and Policies can be found in the syllabus.

On Lecture Notes

Lecture notes included here are works in progress. These were initially typed by John Giorgi as lecture notes for previous course offering. Actual content of lectures may deviate from these notes. However, topics which will be assessed in midterm and final exams will be present in these notes.