Project guidelines have been posted. Please
talk to Ben or Koby before submitting your proposal on Thursday, Feb 15.
Please fill out the questionnaire if you haven't
already and bring to class on Thursday, Jan 11.
Effective modeling of uncertainty and complexity of interactions is a fundamental problem in understanding and designing complex systems. By combining ideas from statistics and graph theory, probabilistic graphical models provide a general representation and an algorithmic framework for reasoning about statistical dependencies. The graphical model family includes very well-known members: Bayesian networks (BNs), Markov random fields (MRFs), hidden Markov models (HMMs), Kalman filters, Ising models, mixture models. These models underly many statistical approaches in computer vision, robotics, natural language understanding, signal processing, computational biology and other fields. The course will cover the following topics:
Part I: Representation
Directed graphical models
Undirected graphical models
Exponential family, generalized linear models
Part II: Inference
Junction tree algorithm
Variational methods, mean field, belief propagation
Sampling methods, Gibbs, MCMC
Part III: Learning
Incomplete data, expectation maximization (EM)
Mixture models, factor analysis, Dirichlet process
Part IV: Student presentations
There is no published textbook for this course. Photocopies of selected chapters from the following books will be handed out in class.
Daphne Koller and Nir Friedman, Structured Probabilistic Models, in preparation.
Michael I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation.
CIS 520 or equivalent. Knowledge of basic probabilty
and statisics, linear algebra, dynamic programming. Exposure to graph theory
and algorithms, information theory, optimization will be helpful.
Attendance, asking and answering questions in class.
In class presentation of a relevant paper. Suggestions to be posted shortly.
Significant independent or group project. There will be a poster session at
the end of the course for project presentations. Projects will also be
evaluated based on midterm and final reports. More...