Schedule overview

The schedule information on this page is subject to changes.

  • Lab

    • Section 1: Tuesdays, 1–4 pm PST

    • Section 2: Tuesdays, 7–10 pm PST

  • Lecture: Wednesdays, 9–9:50 am PST

  • TA recitation: Thursdays, 7-8:30 pm PST

  • TA homework help: Thursdays, 8:30–10 pm PST

  • Instructor office hours: Wednesdays, 2:30-3:30 pm PST

For the first week, all meetings are offered over Zoom.

Unless given notice otherwise, lab sessions, TA recitations, and TA homework help are in Chen 130 and lectures are in Chen 100. Instructor office hours are in usually in Chen 340A, but will occasionally change. They will be announced each week.

Homework due dates

Lesson exercise due dates

Weekly schedule

The notes for each Tuesday lesson must be read ahead of time and associated lesson exercises submitted by 10:30 am PST on the day of the lesson.

  • Week 0
  • Week 1

    • Tu 01/04: First class meeting; no reading.

    • W 01/05: Lesson 01: Probability and scientific logic (lecture)

    • Th 01/06: Recitation 01: Probability review

  • Week 2

    • Tu 01/11: Lesson 02: Plotting posteriors

    • Tu 01/11: Lesson 03: Marginalization by numerical quadrature

    • Tu 01/11: Lesson 04: Conjugacy

    • W 01/12: Lesson 05: Introduction to Bayesian modeling (lecture)

    • Th 01/13: Recitation 02: Review of maximum likelihood estimation

  • Week 3

    • Tu 01/18: Lesson 06: Parameter estimation by optimization

    • W 01/19: Lesson 07: Introduction to Markov chain Monte Carlo (lecture)

    • Th 01/20: Recitation 03: Choosing priors

  • Week 4

    • Tu 01/25: Lesson 08: AWS setup and usage

    • Tu 01/25: Lesson 09: Introduction to MCMC with Stan

    • Tu 01/25: Lesson 10: Mixture models and label switching

    • Tu 01/25: Lesson 11: Regression with Stan

    • W 01/26: Lesson 12: Display of MCMC samples (lecture)

    • Th 01/27: Recitation 04: Introduction to computing with AWS

  • Week 5

    • Tu 02/01: Lesson 13: Model building with prior predictive checks

    • Tu 02/01: Lesson 14: Posterior predictive checks

    • W 02/02: Lesson 15: Collector’s box of distributions (lecture)

    • Th 02/03: Recitation 05: Modeling case study

  • Week 6
  • Week 7

    • Tu 02/15: Lesson 19: Model comparison in practice

    • W 02/16: Lesson 20: Hierarchical models (lecture)

    • Th 02/17: Recitation 07: Background on Hamiltonian Monte Carlo

  • Week 8

    • Tu 02/22: Lesson 21: Implementation of hierarchical models

    • W 02/23: Lesson 22: Principled workflows (lecture)

    • Th 02/24: Recitation 08: Discussion of project proposals

  • Week 9

    • Tu 03/01: Lesson 23: Simulation-based calibration in practice

    • W 03/02: Lesson 24: Introduction to nonparametric Bayes: Gaussian processes

    • Th 03/03: Recitation 09: Sampling out of discrete distributions

  • Week 10

    • Tu 03/08: Lesson 25: Implementation of Gaussian processes

    • W 03/09: Lesson 26: Variational inference

    • W 03/09: Lesson 27: Course wrap-up (lecture)

    • Th 03/10: Recitation 10: Extended homework help