Probabilistic Graphical Models

• Conditional Independence and Factorization. Michael I. Jordan (2003). An Introduction to Probabilistic Graphical Models, Chapter 2 (Section 2.1).

• An Introduction to Conditional Random Fields. Sutton and McCallum (2010).
• Conditional Independence and Factorization. Michael I. Jordan (2003). An Introduction to Probabilistic Graphical Models, Chapter 2 (Section 2.2 - end).

• UGMs: Markov Properties. Pradeep Ravikumar (2020). Lecture Notes.
• DGMs: Markov Properties. Pradeep Ravikumar (2020). Lecture Notes.
• The Elimination Algorithm. Michael I. Jordan (2003). An Introduction to Probabilistic Graphical Models, Chapter 3.

• Probability Propagation and Factor Graphs. Michael I. Jordan (2003). An Introduction to Probabilistic Graphical Models, Chapter 4.
• Inference in Graphical Models. Nowozin and Lampert (2011). Structured Learning and Prediction in Computer Vision, Chapter 3 (Section 3.1).
• Inference in Graphical Models. Nowozin and Lampert (2011). Structured Learning and Prediction in Computer Vision, Chapter 3 (Section 3.2.2).

HW1 out

• An Introduction to Conditional Random Fields. Sutton and McCallum (2010).

• Bidirectional LSTM-CRF Models for Sequence Tagging. Huang, Xu, and Yu (2015).

HW1 due

• [(Section 3 only)] A Tutorial Guide to Mixed-Integer Programming Models and Solution Techniques. Smith & Taskin (2008). Optimization in Medicine and Biology.

HW2 out

• Integer Linear Programming Inference for Conditional Random Fields. Roth and Yih (2005). ICML.

• Structured Prediction. Hal Daumé III (2017). CIML, Chapter 17 (Sections 17 - 17.3).
• Structured Support Vector Machines. Nowozin and Lampert (2011). Structured Learning and Prediction in Computer Vision, Chapter 6.
• Convolutional Networks. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 9.1 - 9.3.

• Monte Carlo Methods. Li (2003). Information Theory, Inference, and Learning Algorithms, Chapter 29 (Section 29.1-29.3).
• Monte Carlo Methods. Li (2003). Information Theory, Inference, and Learning Algorithms, Chapter 29 (Section 29.4 - 29.5).

Project team matching begins

• Monte Carlo Methods. Li (2003). Information Theory, Inference, and Learning Algorithms, Chapter 29 (Section 29.6 - 29.10).

HW2 due

HW3 out

• Probability Propagation and Factor Graphs. Michael I. Jordan (2003). An Introduction to Probabilistic Graphical Models, Chapter 5.

Project team due

HW3 due

HW4 out

• Variational Inference: A Review for Statisticians. Blei, Kucukelbir, and McAuliffe (2018).

Project proposal due

• High-Level Explanation of Variational Inference. Jason Eisner (2011).

HW4 due

HW5 out

• Tutorial on Variational Autoencoders. Carl Doersch (2016).

• Deep Generative Models. Ian Goodfellow, Yoshua Bengio, & Aaron Courville (2016). Deep Learning, Chapter 20.

HW5 due

• A Tutorial on Bayesian Nonparametric Models. Gershman & Blei (2011).

• Regression. Rasmussen & Williams (2006). Gaussian Processes for Machine Learning, Chapter 2.
• A Visual Exploration of Gaussian Processes. Görtler, Kehlbeck, & Deussen (2019).