Introduction to Probabilistic Models for Machine Learning
Instructor: Gaurav Sharma, University of Rochester
Duration: 4 hours total: two 2-hour sessions with a 15-minute break and 30-minute post-class discussion. This class takes place over two days.
Level: Introductory
Course Time:

Prerequisites: Familiarity with basic probability concepts

Benefits

• Describe and intuitively explain fundamental probabilistic concepts such independence, Bayes' rule, and stationarity
• Explain the basis of Maximum Aposteriori Probability (MAP) and Maximum Likelihood (ML) detection and estimation rules
• Describe  how latent variables and sequential dependence underlie expectation maximization and hidden Markov Models
• Develop simple applications of probabilistic models for computer vision and image processing problems
• Cite and explain application examples involving the use of probabilistic models in computer vision, machine learning, and image processing

The course aims to provide attendees a foundation in inference and estimation for machine learning using probabilistic models. Starting from the broad base of probabilistic inference and estimation, the course develops the treatment of specific techniques that underlie many current day machine learning and inference algorithms. Topics covered include a review of concepts from probability and stochastic processes, IID and Markov processes, basics of inference and estimation, Maximum Aposteriori Probability (MAP) and Maximum Likelihood (ML), expectation maximization for ML estimation, hidden Markov models, and Markov and conditional random fields. The pedagogical approach is to illustrate the use of models via concrete examples: each model is introduced via a detailed toy example and then illustrated via one or two actual application examples.
Intended Audience
Engineers, scientists, students, and managers interested in understanding how probabilistic models are used in inference and parameter estimation problems in today's machine learning and computer vision applications and in applying such models to their own problems. Prior familiarity with basics of probability and with matrix vector operations are necessary for a thorough understanding, although attendees lacking this background will still be able to develop an intuitive high-level understanding.

Gaurav Sharma is a professor of electrical and computer engineering and of computer science at the University of Rochester where his research spans signal and image processing, computer vision, color imaging, and bioinformatics. He has extensive experience in media security across diverse application domains. Prior to joining the University of Rochester, he was a principal scientist and project leader at the Xerox Innovation Group. Additionally, he has consulted for several companies on the development of image processing and computer vision algorithms. He holds 54 issued patents and has authored over 195 peer-reviewed publications. He is the editor of the Digital Color Imaging Handbook published by CRC Press. He is the Editor-in-Chief for the IEEE Transactions on Image Processing and previously served as the Editor-in-Chief for the IS&T/SPIE Journal of Electronic Imaging from 2011 through 2015. Sharma is a fellow of IS&T, IEEE, and SPIE.

COST

Discounts given for multiple classes. See Registration page for details and to register.

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