Schedule overview

The schedule information on this page is subject to changes. All times are Pacific.

Homework due dates

  • Homework 1: due 5 pm, January 9
  • Homework 2: due 11:59 pm, January 18
  • Homework 3: due 11:59 pm, January 25
  • Homework 4: due 11:59 pm, February 8
  • Homework 5: due 11:59 pm, February 15
  • Homework 6: due 11:59 pm, February 22
  • Homework 7: due 11:59 pm, March 1
  • Homework 8: due 11:59 pm, March 9

Final exam

  • Final: 9am–noon, March 18

Weekly schedule

The lessons for each Wednesday must be read ahead of time and associated lesson exercise submitted by noon on the Tuesday before.

If one were reading through the lessons, the numbering of the lessons represents the most logical order. However, due to the constraints of class meeting times, some of the lessons are presented out of order. This is not a problem, though, as no lesson that strictly depends on another are presented out of order and the order shown in the schedule below is also a reasonable ordering of the lessons.

Week 0

Week 1

  • M 01/05: Lessons 1–4: Probability as the logic of science (lecture)
  • W 01/07: Lessons 5–9: Sampling and simulation techniques (mostly review and novelty topics; only a cursory read is necessary)

Week 2

  • M 01/12: Lessons 10: Markov chain Monte Carlo (lecture)
  • W 01/14: Lessons 11 and 12: Sampling with Stan

Week 3

  • M 01/19: Martin Luther King Day: No class
  • W 01/21: Lessons 13–18: Bayesian modeling (lecture)

Week 4

  • M 01/26: Review of MCMC and discussion of HMC
  • W 01/28: Lessons 13–18: Bayesian modeling (lecture, continued)
  • W 01/28: Lessons 20–22: Parameter estimation with Markov chain Monte Carlo (lecture)

Week 5

  • M 02/02: Lesson 23 and 24: Posterior predictive checks and mixture models (lecture)
  • W 02/04: Review of modeling and MCMC-based inference

Week 6

  • M 02/09: Lessons 27 and 28: MCMC diagnostics and principled pipelines (lecture)
  • W 02/11: Lesson 29: SBC and related checks in practice

Week 7

  • M 02/16: Presidents Day: No class
  • W 02/19: Lessons 25 and 26: Model comparison (lecture)

Week 8

  • M 02/23: Lesson 30 and 31: Summarizing posteriors with maxima (lecture)
  • W 02/25: Lessons 32–36 are optional reading
  • W 02/25: Variate-covariate models

Week 9

  • M 03/02: Lessons 39–42: Hierarchical models (lecture)
  • W 03/04: Lesson 43: Implementation of hierarchical models

Week 10

  • M 03/09: Course wrap-up (lecture)
  • W 03/11: Choose your model: PCA/PPCA/FA, HMM, GLM, GP

Week 11

  • W 03/18: Final exam, 9 am - noon