10-708 PGM | Schedule

Date	Lecture	Readings	Logistics
Module 1: Introduction, Representation, and Exact Inference
1/14	Lecture #1 (Eric): Introduction to GM [ slides \| video \| notes ]	Jordan, Graphical Models E. Airoldi, Getting Started in Probabilistic Graphical Models
1/16	Lecture #2 (Eric): Representation: Directed GMs (BNs) [ slides \| video \| notes ]	Jordan Textbook, Ch. 2 (Sec. 2.1) Koller and Friedman Textbook, Ch. 3
1/21	No class (MLK day)
1/23	Lecture #3 (Eric): Representation: Undirected GMs (MRFs) [ slides \| video \| notes ]	Jordan Textbook, Ch. 2 (Sec. 2.2 - end) Koller and Friedman Textbook, Ch. 4 A. Fischer and C. Igel, An Introducton to Restricted Boltzmann Machines
1/28	Lecture #4 (Eric): Exact inference - Elimination - Message passing - Sum product algorithm [ slides \| video \| notes ]	Jordan Textbook, Ch. 3 and 4 Koller and Friedman Textbook, Ch. 9, Ch. 10 Tom Minka, Divergence measures and message passing	HW1 out (Mon, 1/28)
1/30	Lecture #5 (skipped): Parameter learning in fully observable Bayesian Networks - Generalized Linear Models (GLIMs) - Maximum Likelihood Estimation (MLE) - Markov Models [ slides \| video \| notes ]	Jordan Textbook, Ch. 8, 9 (Sec. 9.1 - 9.2) Koller and Friedman Textbook, Ch. 17 (Sections 17.1 - 17.4) Geiger and Heckerman, Parameter priors for directed acyclic graphical models and the characterization of several probability distributions Geiger and Heckerman, A characterization of the Dirichlet distribution through global and local parameter independence
2/4	Lecture #6 (Maruan): Parameter Learning of partially observed BN - Mixture models - Hidden Markov Models (HMMs) - The EM algorithm [ slides \| video \| notes ]	Jordan Textbook, Ch. 11 Koller and Friedman Textbook, Ch. 19.1-19.4 Borman, The EM algorithm (A short tutorial) Some interesting aspects of the EM algorithm (Neal and Hinton)
2/6	Lecture #7 (Eric): Maximum likelihood learning of undirected GM [ slides \| video \| notes ]	Jordan Textbook, Ch. 9 (Sec. 9.3-9.5), Ch. 20 Friedman, Hastie, Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Lafferty et al., Conditional Random Fields - Probabilistic Models for Segmenting and Labeling Sequence Data.
2/11	Lecture #8 (guest lecture, Kun Zhang @ Department of Philosophy): Causal discovery and inference [ slides \| video \| notes ]	J. Pearl, M. Glymour, and N. P. Jewell. Causal Inference in Statistics A Primer. Concepts related to causality and identification of causal effects. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Pages 80-89 and 125-123-136. Traditional methods for causal discovery, including the PC and FCI algorithms. K. Zhang, B. Schöklopf, P. Spirtes, and C. Glymour. Learning causality and causality-related learning. Brief review of recent methods for causal discovery.
2/13	Lecture #9 (Eric): Modeling networks - Gaussian graphical models - Ising models [ slides \| video \| notes ]	Meinshausen and Buhlmann, High-Dimensional Graphs and Variable Selection with the Lasso. Kolar et al., Estimating Time-Varying Networks. Dempster, Covariance Selection.	HW1 due (Wed, 2/13)
2/18	Lecture #10 (Eric): Sequential models - Discrete Hidden State (HMM vs. CRF) - Continuous Hidden State (Kalman Filter) [ slides \| video \| notes ]	Jordan Textbook, Ch. 14, Ch. 15 Ng, Lecture notes on factor analysis. Welch and Bishop, An Introduction to the Kalman Filter. Conditional Random Fields - An Introduction.
Module 2: Approximate Inference
2/20	Lecture #11 (Eric): Approximate Inference: Mean Field (MF) and loopy Belief Propagation (BP) approximations [ slides \| video \| notes ]	Yedidia et al., Generalized Belief Propagation. Xing et al., A Generalized Mean Field Algorithm for Variational Inference in Exponential Families. Variational inference tutorial (NIPS 2016)	HW2 out (Fri, 2/22) Project proposal due (Fri, 2/22)
2/25	Lecture #12 (Eric): Theory of Variational Inference: Inner and Outer Approximations [ slides \| video \| notes ]	Wainwright and Jordan, Variational Inference in Graphical Models - The View from the Marginal Polytope. Wainwright and Jordan, Graphical Models, Exponential Families, and Variational Inference (Sec. 3 and 4).
2/27	Lecture #13 (Eric): Approximate Inference: Monte Carlo and Sequential Monte Carlo methods [ slides \| video \| notes ]	Jordan Textbook, Ch. 21 David MacKay's Textbook, Ch. 29 (Sec. 29.1-29.3).
3/4	Lecture #14 (Eric): Markov Chain Monte Carlo - Metropolis-Hastings - Hamiltonian Monte Carlo - Langevin Dynamics [ slides \| video \| notes ]	David MacKay's Textbook, Ch. 29 (Sec. 29.4-29.10). Neal, MCMC using Hamiltonian dynamics. Patterson and Teh, Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex.
Module 3: Deep Learning & Generative Models
3/6	Lecture #15 (Eric): Statistical and Algorithmic Foundations of Deep Learning - Insight into DL - Connections to GM [ slides \| video \| notes ]		HW2 due (Mon, 3/11)
3/11	No classes (Spring break)
3/13	No classes (Spring break)
3/18	Lecture #16 (guest lecture, Zhiting Hu): Building blocks of DL - RNN and LSTM - CNN, Transformers - Attention mechanisms - (Case studies in NLP) [ slides \| video \| notes ]		HW3 out (Mon, 3/18)
3/20	Lecture #17 (Eric): Deep generative models (part 1): Overview of advances and theoretical basis of deep generative models - Wake sleep algorithm - Variational autoencoders - Generative adversarial networks [ slides \| video \| notes ]
3/25	Lecture #18 (guest lecture, Zhiting Hu): Deep generative models (part 2) - Variational Autoencoders (VAE) - Normalizing Flows - Inverse Autoregressive Flows - GANs and Implicit Models [ slides \| video \| notes ]
3/27	Lecture #19 (guest lecture, Zhiting Hu): A unified view of deep generative models - New formulations of deep generative models - Symmetric modeling of latent and visible variables - Evaluation of Deep Generative Models [ slides \| video \| notes ]		Midway report due (Fri, 3/29)
Module 4: Reinforcement Learning & Control Through Inference in GM
4/1	Lecture #20 (Maruan): Sequential decision making (part 1): The framework - Brief introduction to reinforcement learning (RL) - Connections to GM: RL and control as inference [ slides \| video \| notes ]
4/3	Lecture #21 (Maruan): Sequential decision making (part 2): The algorithms - Maximum entropy RL and inverse RL - Max-entropy policy gradient algorithms - Soft Q-learning algorithms - Some open questions/challenges - Applications/case studies (games, robotics, etc.) [ slides \| video \| notes ]		HW3 due (Wed, 4/3)
Module 5: Nonparametric methods
4/8	Lecture #22 (Eric): Bayesian non-parameterics - Dirichlet process (DP) - Hierarchical Dirichlet Process (HDP) - Chinese Restaurant Process (CRP) - Indian Buffet Process (IBP) [ slides \| video \| notes ]		HW4 out (Mon, 4/8)
4/10	Lecture #23 (Maruan): Gaussian processes (GPs) and elements of meta-learning - GPs and (deep) kernel learning - Meta-learning formulation as learning a process - Hypernetworks and contextual networks - Neural processes (NPs) as an approximation to GPs [ slides \| video \| notes ]
4/15	Lecture #24 (Eric): Regularized Bayesian GMs (structured sparsiry, diversity, etc.) [ slides \| video \| notes ]
4/17	Lecture #25 (Eric): Elements of Spectral & Kernel GMs [ slides \| video \| notes ]
Module 6: Modular and scalable algorithms and systems
4/22	Lecture #26 : Automated black-box variational inference and elements of probabilistic programming [ slides \| video \| notes ]		HW4 due (Mon, 4/22)
4/24	Lecture #27 (Eric): Scalable algorithms and systems for learning, inference, and prediction [ slides \| video \| notes ]
4/29	Lecture #28 (Eric): Industialization of AI: standards, modules, building-blocks, and platform [ slides \| video \| notes ]
5/1	Project presentations (TBA)