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 5 pm, January 16
  • Homework 3: due 5 pm, January 23
  • Homework 4: due 5 pm, January 30
  • Homework 5: due 5 pm, February 6
  • Homework 6: due 5 pm, February 13
  • Homework 7: due 5 pm, February 20
  • Homework 8: due 5 pm, February 27
  • Homework 9: due 5 pm, March 6

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: Lessons 20–22: Parameter estimation with Markov chain Monte Carlo (lecture) - W 01/28: Lesson 23 and 24: Posterior predictive checks and mixture models

Week 5

  • M 02/02: Lesson 25: Model comparison
  • W 02/04: Lesson 26: Model comparison in practice

Week 6

  • M 02/09: Lessons 27–29: MCMC diagnostics and principled pipelines
  • W 02/11: Lessons 30 and 31 Summarizing posteriors with maxima (lessons 32–36 are optional reading)

Week 7

  • M 02/16: Presidents Day: No class
  • W 02/19: Variate-covariate models

Week 8

  • M 02/23: Lessons 39–42: Hierarchical models (lecture)
  • W 02/25: Lesson 43: Implementation of hierarchical models

Week 9

  • M 03/02: Lesson numbers TBD: Computational inference topics
  • W 03/04: Choose your model: PCA/PPCA/FA, HMM, GLM, GP

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