# 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.

# 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

# 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

# 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 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