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: Fridays, 2:30-4 pm PST

Unless given notice otherwise, all sessions are at this Zoom link.

Lectures and TA recitations will be recorded and posted at this Google Drive link.

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Homework due dates

• Homework 0: due noon PST, January 4

• Homework 1: due 5 pm PST, January 11

• Homework 2: due 5 pm PST, January 18

• Homework 3: due 5 pm PST, January 25

• Homework 4: due 5 pm PST, February 1

• Homework 5: due 5 pm PST, February 8

• Homework 6: due 5 pm PST, February 15

• Homework 7: due 5 pm PST, February 22

• Homework 8: due 5 pm PST, March 1

• Homework 9: due 5 pm PST, March 8

• Homework 10: due 5 pm PST, March 17

• Homework 11: due 5 pm PST, March 17

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Lesson exercise due dates

• Lesson exercise 1: due 10:30 am PST, January 12

• Lesson exercise 2: due 10:30 am PST, January 19

• Lesson exercise 3: due 10:30 am PST, January 26

• Lesson exercise 4: due 10:30 am PST, February 2

• Lesson exercise 5: due 10:30 am PST, February 9

• Lesson exercise 6: due 10:30 am PST, February 16

• Lesson exercise 7: due 10:30 am PST, February 23

• Lesson exercise 8: due 10:30 am PST, March 2

• Lesson exercise 9: due 10:30 am PST, March 9

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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

  • Lesson 00: Preparing for the course

• Week 1

  • Tu 01/05: First class meeting; no reading.

  • W 01/06: Lesson 01: Probability and scientific logic (lecture)

  • Th 01/07: Recitation 01: Review of maximum likelihood estimation

• Week 2

  • Tu 01/12: Lesson 02: Plotting posteriors

  • Tu 01/12: Lesson 03: Marginalization by numerical quadrature

  • Tu 01/12: Lesson 04: Conjugacy

  • W 01/13: Lesson 05: Introduction to Bayesian modeling (lecture)

  • Th 01/14: Recitation 02: Probability review

• Week 3

  • Tu 01/19: Lesson 06: Parameter estimation by optimization

  • W 01/20: Lesson 07: Introduction to Markov chain Monte Carlo (lecture)

  • Th 01/21: Recitation 03: Choosing priors

• Week 4

  • Tu 01/26: Lesson 08: AWS setup and usage

  • Tu 01/26: Lesson 09: Introduction to MCMC with Stan

  • Tu 01/26: Lesson 10: Mixture models and label switching

  • Tu 01/26: Lesson 11: Regression with Stan

  • W 01/27: Lesson 12: Display of MCMC samples (lecture)

  • Th 01/28: Recitation 04: Introduction to computing with AWS

• Week 5

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

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

  • W 02/03: Lesson 15: Collector’s box of distributions (lecture)

  • Th 02/04: Recitation 05: Modeling case study

• Week 6

  • Tu 02/09: Lesson 16: MCMC diagnostics

  • Tu 02/09: Lesson 17: The Funnel of Hell and uncentering

  • W 02/10: Lesson 18: Model comparison (lecture)

  • Th 02/11: Recitation 06: Practice modeling

• Week 7

  • Tu 02/16: Lesson 19: Model comparison in practice

  • W 02/17: Lesson 20: Hierarchical models (lecture)

  • Th 02/18: Recitation 07: Background on Hamiltonian Monte Carlo

• Week 8

  • Tu 02/23: Lesson 21: Implementation of hierarchical models

  • W 02/24: Lesson 22: Principled workflows (lecture)

  • Th 02/25: Recitation 08: Discussion of project proposals

• Week 9

  • Tu 03/02: Lesson 23: Simulation-based calibration in practice

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

  • Th 03/04: Recitation 09: Sampling out of discrete distributions

• Week 10

  • Tu 03/09: Lesson 25: Implementation of Gaussian processes

  • W 03/10: Lesson 26: Variational inference

  • W 03/10: Lesson 27: Course wrap-up (lecture)

  • Th 03/11: Recitation 10: Gaussian processes