Schedule overview
-----------------

*The schedule information on this page is subject to changes.*

- Lab: Wednesdays, 9 am–noon, Kerckhoff B123
- Lecture: Mondays, 9–10 am, Broad 100
- TA recitation and homework help: Thursdays, 5–7 pm, B123 Kerckhoff
- Instructor office hours: Tuesdays, 2:30-3:30 pm, Kerkchoff B123


----


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

- :ref:`Homework 1<1. Intuitive generative modeling>`: due 5 pm, January 10
- :ref:`Homework 2<2. Analytical and graphical methods for analysis of the posterior>`: due 5 pm, January 17
- :ref:`Homework 3<3. Maximum a posteriori parameter estimation>`: due 5 pm, January 24
- :ref:`Homework 4<4. Sampling with MCMC>`: due 5 pm, January 31
- :ref:`Homework 5<5. Inference with Stan>`: due 5 pm, February 7
- :ref:`Homework 6<6. MCMC with ion channels>`: due 5 pm, February 14
- :ref:`Homework 7<7. Model comparison>`: due 5 pm, February 21
- :ref:`Homework 8<8. Hierarchical models>`: due 5 pm, February 28
- :ref:`Homework 9<9. Principled pipelines and/or VI and/or hierarchical modeling>`: due 5 pm, March 7
- :ref:`Homework 10<10. The grand finale>`: due 5 pm, March 14


----


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

- :ref:`Lesson exercise 1<E1. To be completed after lesson 5>`: due noon, January 14
- :ref:`Lesson exercise 2<E2. To be completed after lesson 6>`: due noon, January 21
- :ref:`Lesson exercise 3<E3. To be completed after lesson 10>`: due noon, January 28
- :ref:`Lesson exercise 4<E4. To be completed after lesson 13>`: due noon, February 4
- :ref:`Lesson exercise 5<E5. To be completed after lesson 16>`: due noon, February 11
- :ref:`Lesson exercise 6<E6. To be completed after lesson 18>`: due noon, February 18
- :ref:`Lesson exercise 7<E7. To be completed after lesson 20>`: due noon, February 25
- :ref:`Lesson exercise 8<E8. To be completed after lesson 22>`: due noon, March 4
- :ref:`Lesson exercise 9<E9. To be completed after lesson 25>`: due noon, March 11


----


Weekly schedule
---------------

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

- **Week 0**
	+ :ref:`Lesson 00<0. Preparing for the course>`: Preparing for the course
- **Week 1**
	+ M 01/06: :ref:`Lesson 01<1. Probability and the logic of scientific reasoning>`: Probability and scientific logic (lecture)
	+ W 01/08: Intuitive modeling (no reading)
- **Week 2**
	+ M 01/13: :ref:`Lesson 02<2. Introduction to Bayesian modeling>`: Introduction to Bayesian modeling (lecture)
	+ W 01/15: :ref:`Lesson 03<3. Plotting posteriors>`: Plotting posteriors
	+ W 01/15: :ref:`Lesson 04<4. Marginalization by numerical quadrature>`: Marginalization by integration
	+ W 01/15: :ref:`Lesson 05<5. Conjugacy>`: Conjugacy
- **Week 3**
	+ M 01/20: No class; Martin Luther King Day
	+ W 01/22: :ref:`Lesson 06<6. Parameter estimation by optimization>`: Parameter estimation by optimization
- **Week 4**
	+ M 01/27: :ref:`Lesson 07<7. Introduction to Markov chain Monte Carlo>`: Introduction to Markov chain Monte Carlo (lecture)
	+ M 01/27: :ref:`Lesson 11<11. Display of MCMC results>`: Display of MCMC samples (lecture)
	+ W 01/29: :ref:`Lesson 08<8. Introduction to MCMC with Stan>`: Introduction to MCMC with Stan
	+ W 01/29: :ref:`Lesson 09<9. Mixture models and label switching with MCMC>`: Mixture models and label switching
	+ W 01/29: :ref:`Lesson 10<10. Variate-covariate models with MCMC>`: Variate-covariate models
- **Week 5**
	+ M 02/03: :ref:`Lesson 14<14. Collector's box of distributions>`: Collector's box of distributions (lecture)
	+ W 02/05: :ref:`Lesson 12<12. Model building with prior predictive checks>`: Model building with prior predictive checks
	+ W 02/05: :ref:`Lesson 13<13. Posterior predictive checks>`: Posterior predictive checks
- **Week 6**
	+ M 02/10: :ref:`Lesson 17<17. Model comparison>`: Model comparison (lecture)
	+ W 02/12: :ref:`Lesson 15<15. MCMC diagnostics>`: MCMC diagnostics
	+ W 02/12: :ref:`Lesson 16<16. A diagnostics case study: Artificial funnel of hell>`: The Funnel of Hell and uncentering
- **Week 7**
	+ M 02/17: :ref:`Lesson 19<19. Hierarchical models>`: Hierarchical models (lecture)
	+ W 02/19: :ref:`Lesson 18<18. Model comparison in practice>`: Model comparison in practice
- **Week 8**
	+ M 02/24: No class; Presidents Day
	+ W 02/26: :ref:`Lesson 21<21. Principled analysis pipelines>`: Principled workflows (lecture)
	+ W 02/26: :ref:`Lesson 20<20. Implementation of hierarchical models>`: Implementation of hierarchical models
- **Week 9**
	+ M 03/03: :ref:`Lesson 25<25. Variational Bayesian inference>`: Variational inference
	+ W 03/05: :ref:`Lesson 22<22: Simulation based calibration and related checks in practice>`: Simulation-based calibration in practice
- **Week 10**
	+ M 03/10: :ref:`Lesson 26<26: Wrap-up>`: Course wrap-up (lecture)
	+ W 03/12: Work on final homework