## Topics in Probabilistic Modeling and Inference |

Instructor: Piyush Rai: (office: RM-502, email: piyush AT cse DOT iitk DOT ac DOT in)

Office Hours: Friday 6:00pm-7:00pm (or by appointment)

Class Venue: KD-101

Class Timings: M/W 5:10-6:30pm

TAs: Shivam Bansal, Dhanajit Brahma, Abhishek Kumar, Vinay Verma

(Information on TA office hours and office locations will be posted on Piazza)

Date |
Topics |
Readings/References/Comments |
Slides/Notes |

Jan 7 | Course Logistics, Intro to Probabilistic Modeling and Inference | [Z15], [B14] (for now, up to sec 3), a brief prob-stats refresher, a basic tutorial on Bayesian inference | slides (print version) |

Jan 9 | Basics of Probabilistic/Bayesian Modeling and Parameter Estimation | Wikipedia entries (to be read in the same order) on Bayesian Inference, Prior, Likelihood, Posterior, Posterior Predictive, Credible Intervals (for now, these articles are meant for cursory reading; may safely skip the parts that seem too advanced to you), Additional Reading: MLAPP Section 3.1-3.3 | slides (print version) |

Jan 12 | Bayesian Inference for Some Basic Models | MLAPP 3.3-3.5, Bayesian Inference for Gaussians, Wikipedia entry on Dirichlet distribution | slides (print version) |

Jan 14 | Bayesian Inference for Gaussians, Working With Gaussians | MLAPP 4.3-4.6 (it is far more detailed than you probably need at the moment; you may skip very detailed proofs, can focus more on the examples and the standard results on Gaussian properties, inference, etc), PRML 2.3, Bayesian Inference for Gaussians | slides (print version) |

Jan 16 | Exponential Family Distributions and Conditional Models | MLAPP 9.1-9.2, some notes on exp-family (if further interested, may skim through the Wikipedia article on exp-fam), PRML 3.3, MLAPP 7.1-7.3, 7.6 (7.6.1-7.6.2) | slides (print version) |

- [B14] Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models: David Blei