Instructor: Aaron Schein
TAs: Wei Kuang, Sounak Paul
Term: Spring 2023
The University of Chicago
- Time: Tuesday and Thursday, 3:30am-4:50pm
- Place: Eckhart room 133
- Office hours:
- Wei: Monday 2-3 pm (Jones 304)
- Sounak: Friday 3-4 pm (Jones 308)
- Assignment 1: Bayesian decision theory. Due Monday April 3 at 11:59pm on GradeScope.
- Assignment 2: Logistic regression and beta-binomial updating. Due Monday April 10 at 11:59pm on GradeScope.
- Assignment 3: Exponential families, conjugacy, and entropy. Due Monday April 17 at 11:59pm on GradeScope.
- Assignment 4: HMMs, belief propagation, variable elimination. Due Tuesday May 2 at 11:59pm on GradeScope.
- Assignment 5: Bayesian mixture models, Gibbs sampling, EM. Due Sunday May 21 at 6:00pm on GradeScope.
- Suggested readings:
- Freedman (1994): "Some issues in the foundations of statistics"
- Freedman "Notes on the Dutch Book argument"
- Chap 15 "The Navy Searches" of The Theory That Would Not Die (see Canvas for e-copy)
- Materials:
- Suggested readings:
- Chap 1 of Berger (1985) Statistical Decision Theory and Bayesian Analysis (see Canvas for e-copy)
- Suggested readings:
- Chap 2 of Hastie et al. (2009) Elements of Statistical Learning
- Materials:
- Suggested readings:
- David Blei's lecture notes on exponential families
- Suggested readings:
- Fong & Holmes (2020) On the marginal likelihood and cross-validation
- Chap 9.1-9.2 of Murphy (2012) Machine Learning: A Probabilistic Perspective
- Chap 14 of John Duchi's lecture notes on info theory
- Suggested readings:
- Chap 1-2, 4.1-4.3, 5.1-5.3, 8 of MacKay (2005) Information Theory, Inference, and Learning Algorithms
- Wikipedia article on Z-channels
- Suggested readings:
- Chap 2 of Jordan (2003) An Introduction to Probabilistic Graphical Models
- David Blei's lecture notes on PGMs
- Suggested readings:
- Chap 3 and chap 4 of Jordan (2003) An Introduction to Probabilistic Graphical Models
- David Blei's lecture notes on inference in PGMs
- Suggested readings:
- David Blei's lecture notes on inference in PGMs
- Scott Linderman's slides on HMMs
- Ramesh Sridharan's lecture notes on HMMs
- Chap 13 (specifically 13.2.2-13.2.4) of Bishop (2006) Pattern Recognition and Machine Learning
- Suggested readings:
- Chap 9 of Bishop (2006) Pattern Recognition and Machine Learning
- Scott Linderman's slides on EM
- Scott Linderman's slides on HMMs
- Suggested readings:
- Scott Linderman's slides on Bayesian mixtures
- David Blei's lecture notes on Bayesian mixtures
- Last lecture's readings
- Suggested readings:
- Chap 11.1.6-11.3 of Bishop (2006) Pattern Recognition and Machine Learning
- David Blei's lecture notes on Bayesian mixtures and Gibbs sampling
- Matthew Stephen's lecture notes on Gibbs sampling
- Scott Linderman's slides on MCMC
- Gregory Gunderson's blogpost on ergodic Markov chains
- Suggested readings:
- Chap 24.2 of Murphy (2012) Machine Learning: A Probabilistic Perspective
- David Blei's lecture notes on Bayesian mixtures and Gibbs sampling
- Stephens (2000): "Dealing with label switching in mixture models"
- Geweke (2004): "Getting it Right: Joint Distribution Tests of Posterior Simulators"
- Roger Grosse's blogpost on Geweke testing
- Suggested readings:
- Blei, Kucukelbir & McAuliffe (2017) "Variational inference: A review for statisticians"
- Chap 10.1-10.2, 10.4 of Bishop (2006) Pattern Recognition and Machine Learning
- Scott Linderman's slides on VI
- Suggested readings:
- Chap 27.3 of Murphy (2012) Machine Learning: A Probabilistic Perspective
- Scott Linderman's slides on CAVI for LDA
- Jeffrey Miller's slides on CAVI for LDA
- Matt Gormley's slides on LDA
- Blei, Ng, Jordan (2003) "Latent Dirichlet Allocation"
- Pritchard, Stephens, Donnelly (2000) "Inference of Population Structure Using Multilocus Genotype Data"
- Suggested readings:
- Lee and Seung (1999) "Learning the parts of objects by non-negative matrix factorization"
- Gillis (2014) "The How and Why of Nonnegative Matrix Factorization"
- Gopalan et al. (2014) "Scalable Recommendation with Poisson Factorization"
- Materials: