CMPE 58K, Sp Top in CmpE: Bayesian Statistics and Machine Learning

Instructor: Ali Taylan Cemgil
Bogaziçi University, Department of Computer Engineering
Istanbul, Turkey

Fall 2008-2009

Course Homepage

http://www-sigproc.eng.cam.ac.uk/~atc27/teaching/cmpe58K

Catalog Description

Machine learning approaches using Bayesian statistics. Graphical models, directed and undirected models, learning and inference, message passing algorithms, Junction Tree, factor graphs, sum-product, hierarchical Bayesian modeling, Monte Carlo methods, MCMC and Sequential Monte Carlo, Expectation-Maximisation, Variational Approximation techniques

Course Description

In the Bayesian paradigm, data is viewed as realizations from highly structured probabilistic models. Once a model is constructed, several interesting problems such as feature extraction, pattern recognition, retrieval, sensor fusion, coding, network analysis, classification, restoration, tracking, source separation or model selection can be formulated as Bayesian inference problems. In this context, graphical models provide a "language" to construct models for quantification of prior knowledge. Unknown parameters in this specification are estimated by probabilistic inference. Often, however, the problem size poses an important challenge and in order to render the approach feasible, specialized inference methods need to be tailored to improve the computational speed and efficiency.
The scope of this course is to review the fundamentals of probabilistic models, inference algorithms and associated data structures. We will review directed (Bayesian Networks) and undirected (Markov Random fields), factor graphs and junction trees. In particular, we will review exact inference, approximate stochastic inference techniques such as Markov Chain Monte Carlo, Sequential Monte Carlo and deterministic (variational) inference techniques. Our ultimate aim is to provide a basic understanding of probabilistic modeling for machine learning, associated computational techniques such that the research students can orient themselves in the relevant literature and understand the current state of the art.

Topics

Textbooks

Handouts and relevant chapters from the following books:

Prerequisite

CmpE 343 (Introductory Probability and Statistics) or equivalent

Administrative (Tentative)



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