Probabilistic Graphical Models
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Information
Probabilistic Graphical Models class officially starts on Monday 19 March. Class webpage is here
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.
Topics include:
- The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities
- Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
- Learning parameters and structure in PGMs
- Using a PGM for decision making under uncertainty.
There will be short weekly review quizzes and programming assignments (Octave/Matlab) focusing on case studies and applications of PGMs to real-world problems:
- Credit scoring and insurance
- Genetic inheritance and disease
- Optical character recognition (OCR)
- Revisiting genetic inheritance / OCR
- Computer vision: Image segmentation
- Decision making and prenatal screening
- Revisiting OCR
- Telling humans apart from aliens with body pose data
- Recognizing human actions from Kinect data
Prerequisites
You should be able to program in at least one programming language and have a computer (Windows, Mac or Linux) with internet access (programming assignments will be conducted in Matlab or Octave). It also helps to have some previous exposure to basic concepts in discrete probability theory (independence, conditional independence, and Bayes' rule).
Here are some exercises that you can do while preparing for the course.
Recommended reading
Textbooks for the 2012 classes - several lists of books, a lot of them free.
Preparation
To prepare for the class in advance, you may consider reading through the following sections of the textbook by Daphne and Nir Friedman:
- Introduction and Overview. Chapters 1, 2.1.1 - 2.1.4, 4.2.1. (Chapter 1 PDF)
- Bayesian Network Fundamentals. Chapters 3.1 - 3.3.
- Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
- Structured CPDs. Chapters 5.1 - 5.5.
- Template Models. Chapters 6.1 - 6.4.1.
These will be covered in the first two weeks of the online class.
