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 <http://bebi103.caltech.edu/2021/b/zoom_links/bebi103_main>`_. 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. .. 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/>`_. ---- Homework due dates ------------------ - :ref:`Homework 0<Homework 0.1: Configure your team (0 pts)>`: due at noon PST, January 3 - :ref:`Homework 1<1. Intuitive generative modeling>`: due 5 pm PST, January 14 - :ref:`Homework 2<2. Analytical and graphical methods for analysis of the posterior>`: due 5 pm PST, January 21 - :ref:`Homework 3<3. Maximum a posteriori parameter estimation>`: due 5 pm PST, January 28 - :ref:`Homework 4<4. Sampling with MCMC>`: due 5 pm PST, February 4 - :ref:`Homework 5<5. Inference with Stan>`: due 5 pm PST, February 11 - :ref:`Homework 6<6. Practice building and assessing Bayesian models>`: due 5 pm PST, February 18 - :ref:`Homework 7<7. Model comparison>`: due 5 pm PST, February 25 - :ref:`Homework 8<8. Hierarchical models>` (team portion): due 5 pm PST, March 4 - :ref:`Homework 8<8. Hierarchical models>` (solo portion): due 5 pm PST, March 11 - :ref:`Homework 9<9. Principled pipelines>`: due 5 pm PST, March 11 - :ref:`Homework 10<10. The grand finale>`: due 5 pm PST, March 15 - :ref:`Homework 11<11. Course feedback>`: due 5 pm PST, March 16 ---- Lesson exercise due dates ------------------------- - :ref:`Lesson exercise 1<E1. To be completed after lesson 4>`: due 10:30 am PST, January 11 - :ref:`Lesson exercise 2<E2. To be completed after lesson 6>`: due 10:30 am PST, January 18 - :ref:`Lesson exercise 3<E3. To be completed after lesson 11>`: due 10:30 am PST, January 25 - :ref:`Lesson exercise 4<E4. To be completed after lesson 14>`: due 10:30 am PST, February 1 - :ref:`Lesson exercise 5<E5. To be completed after lesson 17>`: due 10:30 am PST, February 8 - :ref:`Lesson exercise 6<E6. To be completed after lesson 19>`: due 10:30 am PST, February 15 - :ref:`Lesson exercise 7<E7. To be completed after lesson 21>`: due 10:30 am PST, February 22 - :ref:`Lesson exercise 8<E8. To be completed after lesson 23>`: due 10:30 am PST, March 1 - :ref:`Lesson exercise 9<E9. To be completed after lesson 25>`: 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** + :ref:`Lesson 00<0. Preparing for the course>`: Preparing for the course - **Week 1** + Tu 01/04: First class meeting; no reading. + W 01/05: :ref:`Lesson 01<1. Probability and the logic of scientific reasoning>`: Probability and scientific logic (lecture) + Th 01/06: Recitation 01: Probability review - **Week 2** + Tu 01/11: :ref:`Lesson 02<2. Plotting posteriors>`: Plotting posteriors + Tu 01/11: :ref:`Lesson 03<3. Marginalization by numerical quadrature>`: Marginalization by numerical quadrature + Tu 01/11: :ref:`Lesson 04<4. Conjugacy>`: Conjugacy + W 01/12: :ref:`Lesson 05<5. Introduction to Bayesian modeling>`: Introduction to Bayesian modeling (lecture) + Th 01/13: Recitation 02: Review of maximum likelihood estimation - **Week 3** + Tu 01/18: :ref:`Lesson 06<6. Parameter estimation by optimization>`: Parameter estimation by optimization + W 01/19: :ref:`Lesson 07<7. Introduction to Markov chain Monte Carlo>`: Introduction to Markov chain Monte Carlo (lecture) + Th 01/20: Recitation 03: Choosing priors - **Week 4** + Tu 01/25: :ref:`Lesson 08<8. Cloud computing setup and usage>`: AWS setup and usage + Tu 01/25: :ref:`Lesson 09<9. Introduction to MCMC with Stan>`: Introduction to MCMC with Stan + Tu 01/25: :ref:`Lesson 10<10. Mixture models and label switching with MCMC>`: Mixture models and label switching + Tu 01/25: :ref:`Lesson 11<11. Regression with MCMC>`: Regression with Stan + W 01/26: :ref:`Lesson 12<12. Display of MCMC results>`: Display of MCMC samples (lecture) + Th 01/27: Recitation 04: Introduction to computing with AWS - **Week 5** + Tu 02/01: :ref:`Lesson 13<13. Model building with prior predictive checks>`: Model building with prior predictive checks + Tu 02/01: :ref:`Lesson 14<14. Posterior predictive checks>`: Posterior predictive checks + W 02/02: :ref:`Lesson 15<15. Collector's box of distributions>`: Collector's box of distributions (lecture) + Th 02/03: :ref:`Recitation 05<R5. A Bayesian modeling case study: Ant traffic jams>`: Modeling case study - **Week 6** + Tu 02/08: :ref:`Lesson 16<16. MCMC diagnostics>`: MCMC diagnostics + Tu 02/08: :ref:`Lesson 17<17. A diagnostics case study: Artificial funnel of hell>`: The Funnel of Hell and uncentering + W 02/09: :ref:`Lesson 18<18. Model comparison>`: Model comparison (lecture) + Th 02/10: :ref:`Recitation 06<R6. Practice model building>`: Practice modeling - **Week 7** + Tu 02/15: :ref:`Lesson 19<19. Model comparison in practice>`: Model comparison in practice + W 02/16: :ref:`Lesson 20<20. Hierarchical models>`: Hierarchical models (lecture) + Th 02/17: :ref:`Recitation 07<R7. Introduction to Hamiltonian Monte Carlo>`: Background on Hamiltonian Monte Carlo - **Week 8** + Tu 02/22: :ref:`Lesson 21<21. Implementation of hierarchical models>`: Implementation of hierarchical models + W 02/23: :ref:`Lesson 22<22. Principled analysis pipelines>`: Principled workflows (lecture) + Th 02/24: :ref:`Recitation 08<R8: Discussion of HW 10 project proposals>`: Discussion of project proposals - **Week 9** + Tu 03/01: :ref:`Lesson 23<23: Simulation based calibration and related checks in practice>`: Simulation-based calibration in practice + W 03/02: :ref:`Lesson 24<24. Introduction to Gaussian processes>`: Introduction to nonparametric Bayes: Gaussian processes + Th 03/03: :ref:`Recitation 09<R9: Sampling discrete parameters with Stan>`: Sampling out of discrete distributions - **Week 10** + Tu 03/08: :ref:`Lesson 25<25. Implementation of Gaussian processes>`: Implementation of Gaussian processes + W 03/09: :ref:`Lesson 26<26: Variational Bayesian inference>`: Variational inference + W 03/09: :ref:`Lesson 27<27: Wrap-up>`: Course wrap-up (lecture) + Th 03/10: Recitation 10: Extended homework help