Date Lecture Readings Logistics
Module 1: Introduction, Representation, and Exact Inference
1/14 Lecture #1 (Eric):
Introduction to GM
[ slides (annotated) | video | notes ]

1/16 Lecture #2 (Eric):
Representation: Directed GMs (BNs)
[ slides (annotated) | 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 (annotated) | video | notes ]

1/28 Lecture #4 (Eric):
Exact inference
- Elimination
- Message passing
- Sum product algorithm
[ slides (annotated) | video | notes ]

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 ]

2/4 Lecture #6 (Maruan):
Parameter Learning of partially observed BN
- Mixture models
- Hidden Markov Models (HMMs)
- The EM algorithm
[ slides | video | notes ]

2/6 Lecture #7 (Eric):
Maximum likelihood learning of undirected GM
[ slides (annotated) | video | notes ]

2/11 Lecture #8 (guest lecture, Kun Zhang @ Department of Philosophy):
Causal discovery and inference
[ slides | video | notes ]

2/13 Lecture #9 (Eric):
Modeling networks
- Gaussian graphical models
- Ising models
[ slides (annotated) | video | notes ]

HW1 due (Wed, 2/13)

2/18 Lecture #10 (Eric):
Sequential models
- Discrete Hidden State (HMM vs. CRF)
- Continuous Hidden State (Kalman Filter)
[ slides (annotated) | video | notes ]

Module 2: Approximate Inference
2/20 Lecture #11 (Eric):
Approximate Inference: Mean Field (MF) and loopy Belief Propagation (BP) approximations
[ slides (annotated) | video | notes ]

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 (annotated) | video | notes ]

2/27 Lecture #13 (Eric):
Approximate Inference: Monte Carlo and Sequential Monte Carlo methods
[ slides (annotated) | 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 (annotated) | video | notes ]

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)