Fall 2018

Columbia University

- Description and Syllabus
- Instructor: David M. Blei (office hours]
- Teaching Assistant: Keyon Vafa (Office hours: Tue 2-4PM in DSI)
- Meeting: Tue/Thu 8:40AM-9:55AM, Havermeyer 209
- Piazza site
- Final project guidelines
- LaTeX template

Below are the topics of the class and the readings. These topics may span multiple lectures. Some readings are not yet set, but this list will be kept up to date. The readings from Blei (2018) will be made available to the class.

**Introduction**- "Build, compute, critique, repeat: Data analysis with latent variable models" (Blei, 2014)
- Slides

**The ingredients of probabilistic models**- "Model-based machine learning" (Bishop, 2013)
- "Some issues in the foundations of statistics" (Freedman, 1994)
- "Chapter 2: The ingredients of probabilistic models" (Blei, 2018)

**Bayesian mixture models and the Gibbs sampler**- "Chapter 3: Bayesian mixture models (and an introduction to Gibbs sampling)" (Blei, 2018)
- "Identifying Bayesian mixture models" (Betancourt, 2018)

**Mixed-membership models, topic models, and variational inference**- "Probabilistic topic models" (Blei, 2012)

**Matrix factorization and efficient MAP inference**- "Matrix factorization techniques for recommender systems" (Koren et al., 2009)

**Deep generative models and black box variational inference**- "Stochastic backpropagation and approximate inference in deep generative models" (Rezende et al., 2014)
- "Autoencoding variational Bayes" (Kingma and Welling, 2013)
- "Deep exponential families" (Ranganath et al., 2015)

**Time series and sequence models****Exponential families, conjugate priors, and generalized linear models****Hierarchical models, robust models, and empirical Bayes****The theory of graphical models****Advanced topics in variational inference**- "Variational inference: A review for statisticians" (Blei et al., 2017)

**Model checking and model diagnosis**- "Posterior predictive assessment of model fitness via realized discrepancies" (Gelman et al., 1996)

**An introduction to causality**