# 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

Homework 0: due at noon PST, January 3

Homework 1: due 5 pm PST, January 14

Homework 2: due 5 pm PST, January 21

Homework 3: due 5 pm PST, January 28

Homework 4: due 5 pm PST, February 4

Homework 5: due 5 pm PST, February 11

Homework 6: due 5 pm PST, February 18

Homework 7: due 5 pm PST, February 25

Homework 8 (team portion): due 5 pm PST, March 4

Homework 8 (solo portion): due 5 pm PST, March 11

Homework 9: due 5 pm PST, March 11

Homework 10: due 5 pm PST, March 15

Homework 11: due 5 pm PST, March 16

# Lesson exercise due dates

Lesson exercise 1: due 10:30 am PST, January 11

Lesson exercise 2: due 10:30 am PST, January 18

Lesson exercise 3: due 10:30 am PST, January 25

Lesson exercise 4: due 10:30 am PST, February 1

Lesson exercise 5: due 10:30 am PST, February 8

Lesson exercise 6: due 10:30 am PST, February 15

Lesson exercise 7: due 10:30 am PST, February 22

Lesson exercise 8: due 10:30 am PST, March 1

Lesson exercise 9: due 10:30 am PST, March 7

# 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 01/04: First class meeting; no reading.

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

Th 01/06: Recitation 01: Probability review

**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**Tu 02/08: Lesson 16: MCMC diagnostics

Tu 02/08: Lesson 17: The Funnel of Hell and uncentering

W 02/09: Lesson 18: Model comparison (lecture)

Th 02/10: Recitation 06: Practice modeling

**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