Instructor: Scott Linderman
TA: Matt MacKay, James Yang
Term: Spring 2022
Stanford University
Probabilistic modeling and inference of multivariate data. Topics may include multivariate Gaussian models, probabilistic graphical models, MCMC and variational Bayesian inference, dimensionality reduction, principal components, factor analysis, matrix completion, topic modeling, and state space models. Extensive work with data involving programming, ideally in Python.
Students should be comfortable with probability and statistics as well as multivariate calculus and linear algebra. This course will emphasize implementing models and algorithms, so coding proficiency is required.
- Time: Monday and Wednesday, 11:30am-1pm
- Level: advanced undergrad and up
- Grading basis: credit or letter grade
- Office hours:
- Monday 1-2pm (Scott)
- Tuesday 5:30-7pm in Bowker, Room 207, Sequoia Hall and over Zoom (Matt)
- Friday 1-2:30pm Zoom (James)
- Final evaluation: Exam
- Bishop. Pattern recognition and machine learning. New York: Springer, 2006. link
- Murphy. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023. link
- Gelman et al. Bayesian Data Analysis. Chapman and Hall, 2005. link
- Assignment 1: Bayesian Linear Regression. Due Weds. Apr 6 at 11:59pm on GradeScope.
- Assignment 2: Gibbs Sampling and Metropolis-Hastings. Due Weds. Apr 13 at 11:59pm on GradeScope.
- Assignment 3: Continuous Latent Variable Models. Due Weds. Apr 20 at 11:59pm on GradeScope.
- Assignment 4: Bayesian Mixture Models. Due Weds. Apr 27 at 11:59pm on GradeScope.
- Assignment 5: Poisson Matrix Factorization. Due Weds. May 4 at 11:59pm on GradeScope.
- Assignment 6: Neural Networks and VAEs. Due Weds. May 11 at 11:59pm on GradeScope.
- Assignment 7: Hidden Markov Models. Due Weds. May 18 at 11:59pm on GradeScope.
- Required Reading: Bishop, Ch 2.3
- Optional Reading: Murphy, Ch 2.3 and 3.2.4
- Required Reading: Bishop, Ch 8.1-8.2 and 11.2-11.3
- Optional Reading: Murphy, Ch 3.5.2, 4.2, and 11.1-11.3
- Optional Reading: Gelman, Ch 5
- Required Reading: Bishop, Ch 12.1-12.2
- Required Reading: MCMC using Hamiltonian dynamics Neal, 2012
- Required Reading: Bishop, Ch 9
- Optional Reading: Murphy, Ch 6.7
- Required Reading: "Probabilistic topic models" Blei, 2012
- Required Reading: "Variational Inference: A Review for Statisticians” Blei et al, 2017
- Optional Reading: Murphy, Ch 10.2
- Required Reading: “An Introduction to Variational Autoencoders” (Ch 1 and 2) Kingma and Welling, 2019
- Optional Reading: Murphy, Ch 10.3
- Required Reading: Bishop, Ch 13
- Optional Reading: Murphy, Ch 8
- Required Reading: Bishop, Ch 6.4
- Optional Reading: Kingman, 1993, Ch 1-2
- Optional Reading: Adams et al, 2019
- TBD