Schedule overview ----------------- *The schedule information on this page is subject to changes.* - Lab + Section 1: Wednesdays, 9 am–noon, Broad 200 + Section 2: Wednesdays, 1–4 pm, Chen 240a - Lecture + Section 1: Mondays, 9–10 am, Broad 100 + Section 2: Mondays, 10–11 am, Broad 100 - TA recitation: Thursdays, 7–8:30 pm, Chen 100 - TA homework help: Tuesdays, 2:30–4 pm, Broad 200 - Extra TA homework help: Thursdays, 8:30–10 pm, Chen 100 - Instructor office hours: Mondays, 2-3 pm, Kerkchoff B123 ---- Homework due dates ------------------ - :ref:`Homework 1<1. Intuitive generative modeling>`: due 5 pm, January 12 - :ref:`Homework 2<2. Analytical and graphical methods for analysis of the posterior>`: due 5 pm, January 19 - :ref:`Homework 3<3. Maximum a posteriori parameter estimation>`: due 5 pm, January 26 - :ref:`Homework 4<4. Sampling with MCMC>`: due 5 pm, February 2 - :ref:`Homework 5<5. Inference with Stan>`: due 5 pm, February 9 - :ref:`Homework 6<6. MCMC with ion channels>`: due 5 pm, February 16 - :ref:`Homework 7<7. Model comparison>`: due 5 pm, February 23 - :ref:`Homework 8<8. Hierarchical models>`: due 5 pm, March 1 - :ref:`Homework 9<9. Principled pipelines and/or VI and/or hierarchical modeling>`: due 5 pm, March 8 - :ref:`Homework 10<10. The grand finale>`: due 4 pm, March 15 - :ref:`Homework 11<11. Course feedback>`: due 5 pm, March 15 ---- Lesson exercise due dates ------------------------- - :ref:`Lesson exercise 1`: due noon, January 9 - :ref:`Lesson exercise 2`: due noon, January 16 - :ref:`Lesson exercise 3`: due noon, January 23 - :ref:`Lesson exercise 4`: due noon, January 30 - :ref:`Lesson exercise 5`: due noon, February 6 - :ref:`Lesson exercise 6`: due noon, February 13 - :ref:`Lesson exercise 7`: due noon, February 20 - :ref:`Lesson exercise 8`: due noon, February 27 - :ref:`Lesson exercise 9`: due noon, March 5 ---- 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** + W 01/03: :ref:`Lesson 01<1. Probability and the logic of scientific reasoning>`: Probability and scientific logic (lecture) - **Week 2** + M 01/08: :ref:`Lesson 02<2. Introduction to Bayesian modeling>`: Introduction to Bayesian modeling (lecture) + W 01/10: :ref:`Lesson 03<3. Plotting posteriors>`: Plotting posteriors + W 01/10: :ref:`Lesson 04<4. Marginalization by numerical quadrature>`: Marginalization by integration + W 01/10: :ref:`Lesson 05<5. Conjugacy>`: Conjugacy + Th 01/11: :ref:`Recitation 01`: Probability review - **Week 3** + M 01/15: No class; Martin Luther King Day + W 01/17: :ref:`Lesson 07<7. Introduction to Markov chain Monte Carlo>`: Introduction to Markov chain Monte Carlo (lecture) + W 01/17: :ref:`Lesson 06<6. Parameter estimation by optimization>`: Parameter estimation by optimization + Th 01/18: :ref:`Recitation 02`: Choosing priors and review of optimization - **Week 4** + M 01/22: :ref:`Lesson 11<11. Display of MCMC results>`: Display of MCMC samples (lecture) + W 01/24: :ref:`Lesson 08<8. Introduction to MCMC with Stan>`: Introduction to MCMC with Stan + W 01/24: :ref:`Lesson 09<9. Mixture models and label switching with MCMC>`: Mixture models and label switching + W 01/24: :ref:`Lesson 10<10. Variate-covariate models with MCMC>`: Regression with Stan + Th 01/25: No recitation; only homework help - **Week 5** + M 01/29: :ref:`Lesson 14<14. Collector's box of distributions>`: Collector's box of distributions (lecture) + W 01/31: :ref:`Lesson 12<12. Model building with prior predictive checks>`: Model building with prior predictive checks + W 01/31: :ref:`Lesson 13<13. Posterior predictive checks>`: Posterior predictive checks + Th 02/01: :ref:`Recitation 04`: Introduction to Hamiltonian Monte Carlo - **Week 6** + M 02/05: :ref:`Lesson 17<17. Model comparison>`: Model comparison (lecture) + W 02/07: :ref:`Lesson 15<15. MCMC diagnostics>`: MCMC diagnostics + W 02/07: :ref:`Lesson 16<16. A diagnostics case study: Artificial funnel of hell>`: The Funnel of Hell and uncentering + Th 02/08: :ref:`Recitation 05`: Practice model building - **Week 7** + M 02/12: :ref:`Lesson 19<19. Hierarchical models>`: Hierarchical models (lecture) + W 02/14: :ref:`Lesson 18<18. Model comparison in practice>`: Model comparison in practice + Th 02/15: :ref:`Recitation 06`: MCMC using Caltech's HPC - **Week 8** + M 02/19: No class; Presidents Day + W 02/21: :ref:`Lesson 21<21. Principled analysis pipelines>`: Principled workflows (lecture) + W 02/21: :ref:`Lesson 20<20. Implementation of hierarchical models>`: Implementation of hierarchical models + Th 02/22: :ref:`Recitation 07`: Sampling discrete parameters with Stan - **Week 9** + M 02/26: :ref:`Lesson 25<25. Variational Bayesian inference>`: Variational inference + W 02/28: :ref:`Lesson 22<22: Simulation based calibration and related checks in practice>`: Simulation-based calibration in practice + Th 02/29: :ref:`Recitation 08`: Discussion of HW 10 project proposals - **Week 10** + M 03/04: :ref:`Lesson 26<26: Wrap-up>`: Course wrap-up (lecture) + W 03/06: Work on final homework + Th 03/08: No recitation; only homework help