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 <http://bebi103.caltech.edu/2021/b/zoom_links/bebi103_main>`_.

Lectures and TA recitations will be recorded and posted at `this Google Drive link <http://bebi103.caltech.edu/2021/b/recordings/google_drive/>`_.


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Homework due dates
------------------

- :ref:`Homework 0<Homework 0.1: Configure your team (5 pts)>`: due noon PST, January 4
- :ref:`Homework 1<1. Intuitive generative modeling>`: due 5 pm PST, January 11
- :ref:`Homework 2<2. Analytical and graphical methods for analysis of the posterior>`: due 5 pm PST, January 18
- :ref:`Homework 3<3. Maximum a posteriori parameter estimation>`: due 5 pm PST, January 25
- :ref:`Homework 4<4. Sampling with MCMC>`: due 5 pm PST, February 1
- :ref:`Homework 5<5. Inference with Stan I>`: due 5 pm PST, February 8
- :ref:`Homework 6<6. Practice building and assessing Bayesian models>`: due 5 pm PST, February 15
- :ref:`Homework 7<7. Model comparison>`: due 5 pm PST, February 22
- :ref:`Homework 8<8. Hierarchical models>`: due 5 pm PST, March 1
- :ref:`Homework 9<9. Principled pipelines and hierarchical modeling of noise>`: due 5 pm PST, March 8
- :ref:`Homework 10<10. The grand finale>`: due 5 pm PST, March 17
- :ref:`Homework 11<11. Course feedback>`: due 5 pm PST, March 17


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Lesson exercise due dates
-------------------------

- :ref:`Lesson exercise 1<E1. To be completed after lesson 4>`: due 10:30 am PST, January 12
- :ref:`Lesson exercise 2<E2. To be completed after lesson 6>`: due 10:30 am PST, January 19
- :ref:`Lesson exercise 3<E3. To be completed after lesson 11>`: due 10:30 am PST, January 26
- :ref:`Lesson exercise 4<E4. To be completed after lesson 14>`: due 10:30 am PST, February 2
- :ref:`Lesson exercise 5<E5. To be completed after lesson 17>`: due 10:30 am PST, February 9
- :ref:`Lesson exercise 6<E6. To be completed after lesson 19>`: due 10:30 am PST, February 16
- :ref:`Lesson exercise 7<E7. To be completed after lesson 21>`: due 10:30 am PST, February 23
- :ref:`Lesson exercise 8<E8. To be completed after lesson 23>`: due 10:30 am PST, March 2
- :ref:`Lesson exercise 9<E9. To be completed after lesson 25>`: 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**
	+ :ref:`Lesson 00<0. Preparing for the course>`: Preparing for the course
- **Week 1**
	+ Tu 01/05: First class meeting; no reading.
	+ W 01/06: :ref:`Lesson 01<1. Probability and the logic of scientific reasoning>`: Probability and scientific logic (lecture)
	+ Th 01/07: Recitation 01: Review of maximum likelihood estimation
- **Week 2**
	+ Tu 01/12: :ref:`Lesson 02<2. Plotting posteriors>`: Plotting posteriors
	+ Tu 01/12: :ref:`Lesson 03<3. Marginalization by numerical quadrature>`: Marginalization by numerical quadrature
	+ Tu 01/12: :ref:`Lesson 04<4. Conjugacy>`: Conjugacy
	+ W 01/13: :ref:`Lesson 05<5. Introduction to Bayesian modeling>`: Introduction to Bayesian modeling (lecture)
	+ Th 01/14: Recitation 02: Probability review
- **Week 3**
	+ Tu 01/19: :ref:`Lesson 06<6. Parameter estimation by optimization>`: Parameter estimation by optimization
	+ W 01/20: :ref:`Lesson 07<7. Introduction to Markov chain Monte Carlo>`: Introduction to Markov chain Monte Carlo (lecture)
	+ Th 01/21: Recitation 03: Choosing priors
- **Week 4**
	+ Tu 01/26: :ref:`Lesson 08<8. AWS setup and usage>`: AWS setup and usage
	+ Tu 01/26: :ref:`Lesson 09<9. Introduction to MCMC with Stan>`: Introduction to MCMC with Stan
	+ Tu 01/26: :ref:`Lesson 10<10. Mixture models and label switching with MCMC>`: Mixture models and label switching
	+ Tu 01/26: :ref:`Lesson 11<11. Regression with MCMC>`: Regression with Stan
	+ W 01/27: :ref:`Lesson 12<12. Display of MCMC results>`: Display of MCMC samples (lecture)
	+ Th 01/28: Recitation 04: Introduction to computing with AWS
- **Week 5**
	+ Tu 02/02: :ref:`Lesson 13<13. Model building with prior predictive checks>`: Model building with prior predictive checks
	+ Tu 02/02: :ref:`Lesson 14<14. Posterior predictive checks>`: Posterior predictive checks
	+ W 02/03: :ref:`Lesson 15<15. Collector's box of distributions>`: Collector's box of distributions (lecture)
	+ Th 02/04: :ref:`Recitation 05<R5. A Bayesian modeling case study: Ant traffic jams>`: Modeling case study
- **Week 6**
	+ Tu 02/09: :ref:`Lesson 16<16. MCMC diagnostics>`: MCMC diagnostics
	+ Tu 02/09: :ref:`Lesson 17<17. A diagnostics case study: Artificial funnel of hell>`: The Funnel of Hell and uncentering
	+ W 02/10: :ref:`Lesson 18<18. Model comparison>`: Model comparison (lecture)
	+ Th 02/11: :ref:`Recitation 06<R6. Practice model building>`: Practice modeling
- **Week 7**
	+ Tu 02/16: :ref:`Lesson 19<19. Model comparison in practice>`: Model comparison in practice
	+ W 02/17: :ref:`Lesson 20<20. Hierarchical models>`: Hierarchical models (lecture)
	+ Th 02/18: :ref:`Recitation 07<R7. Introduction to Hamiltonian Monte Carlo>`: Background on Hamiltonian Monte Carlo
- **Week 8**
	+ Tu 02/23: :ref:`Lesson 21<21. Implementation of hierarchical models>`: Implementation of hierarchical models
	+ W 02/24: :ref:`Lesson 22<22. Principled analysis pipelines>`: Principled workflows (lecture)
	+ Th 02/25: :ref:`Recitation 08<R8: Discussion of HW 10 project proposals>`: Discussion of project proposals
- **Week 9**
	+ Tu 03/02: :ref:`Lesson 23<23: Simulation based calibration and related checks in practice>`: Simulation-based calibration in practice
	+ W 03/03: :ref:`Lesson 24<24. Introduction to Gaussian processes>`: Introduction to nonparametric Bayes: Gaussian processes
	+ Th 03/04: :ref:`Recitation 09<R9: Sampling discrete parameters with Stan>`: Sampling out of discrete distributions
- **Week 10**
	+ Tu 03/09: :ref:`Lesson 25<25. Implementation of Gaussian processes>`: Implementation of Gaussian processes
	+ W 03/10: :ref:`Lesson 26<26: Variational Bayesian inference>`: Variational inference
	+ W 03/10: :ref:`Lesson 27<27: Wrap-up>`: Course wrap-up (lecture)
	+ Th 03/11: Recitation 10: Gaussian processes