Course Calendar
Download lecture slides by clicking on the corresponding lecture title. Lecture topics (right) link to suggested readings below.
No Lecture
October 11, 2016
Begin to form your project team & define your project area.
Video Lecture:
Monte Carlo
October 13, 2016
Thanksgiving
(No Lecture)
November 24, 2016
Project Presentations
December 13, 2016
Beginning at 2:30pm in CIT 368.
Suggested Readings
We provide suggested readings from several sources. You do not need to read all of them. Instead, we suggest you compare various options, and choose the resource whose style you like best. Barber's Bayesian Reasoning and Machine Learning is freely available online, and is a good place to start.
Acronyms for Primary Resources
- BRML: Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press 2012. Free online.
- MLaPP: Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press 2012. Excerpt online.
- PRML: Pattern Recognition and Machine Learning, Christopher Bishop, Springer 2007. Excerpt online.
- GEV: Graphical Models, Exponential Families, and Variational Inference, Martin Wainwright & Michael Jordan, Foundations & Trends in Machine Learning, 2008.
- EBS: Graphical Models for Visual Object Recognition and Tracking, Erik B. Sudderth, PhD Thesis (Chapter 2), MIT 2006.
Graphical Model Tutorials
Directed & Undirected Graphs: Factorization & Markov Properties
- BRML: Chapters 2-4, excluding Sec. 3.4.
- MLaPP: Sec. 10.1-10.2, 10.5, 19.1-19.4.
- PRML: Sec. 8.1-8.3.
- GEV: Sec. 2.1-2.4.
- EBS: Sec. 2.2.1-2.2.3.
Inference via Variable Elimination
Inference via Belief Propagation: Sum-Product & Max-Product
Inference via Junction Tree Propagation
Exponential Family Distributions: Learning & Inference
- BRML: Chapter 8 excluding Sec. 8.4, 8.8.
- MLaPP: Sec. 9.2.
- PRML: Sec. 2.4.
- GEV: Chapter 3.
- EBS: Sec. 2.1.
Learning (Directed & Undirected) Graphical Model Parameters
- BRML: Chapter 9 excluding Sec. 9.5.
- MLaPP: Sec. 10.4, 19.5.
- GEV: Sec. 6.1.
Learning via the Expectation Maximization (EM) Algorithm
Learning (Directed & Undirected) Graphical Model Structure
- BRML: Sec. 9.5-9.6, 12.1-12.3, 12.5.
- MLaPP: Chapter 26 excluding Sec. 26.5-26.7.
Inference & Learning for Gaussian Graphical Models
Monte Carlo Methods: Rejection & Importance Sampling
Particles & Sequential Monte Carlo
- BRML: Sec. 27.6.
- MLaPP: Sec. 23.5.
- An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo, Cappe, Godsill, & Moulines, IEEE 2007.
- Nonparametric Belief Propagation, Sudderth, Ihler, Isard, Freeman, & Willsky, CACM 2010.
Markov Chain Monte Carlo (MCMC): Gibbs & Metropolis-Hastings
Variational Methods: Naive & Structured Mean Field
Variational Methods: Bethe Approximations, Loopy & Reweighted BP
Discriminative Learning: Conditional Random Fields & Structural SVMs
Neural Networks & Deep Learning
Bayesian Nonparametrics: Dirichlet Processes