ITL Class

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Welcome to EEL 6935 - Information Theoretic Learning

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Graduate

Prereq: EEL 6502 or approval of instructor.

Catalog

Fundamentals of adaptive systems training and information theory. Renyi’s entropy definition. Sample estimators of Renyi’s entropy. Cost functions based on entropy and divergence and their corresponding training algorithms. Reproducing Kernel Hilbert spaces (RKHS) and ITL. Correntropy as a new similarity measure. Statistical signal processing in RKHS. Applications in signal processing and machine learning

Text Book

Instructor notes (being compiled in a book).

References

Haykin S., Adaptive Filter Theory, Prentice Hall (4th edition)
Cover T., Elements of Information Theory, Wiley
Debnath D., Hilbert Spaces with Applications, Academic Press
Scholkopf B., et al, Advances in Kernel Methods, MIT Press

Professor

Jose C. Principe, Professor of Electrical Engineering. Office EB 451, email: principe@cnel.ufl.edu

Goals

Understand the principles of inference to implement nonlinear signal processing algorithms. New cost functions to train adaptive signal processing algorithms will be presented and compared with conventional adaptive filters. Links between ITL and reproducing kernel Hilbert spaces will be established. New RKHS will be presented that are specifically useful for statistical signal processing and machine learning.

Grading

Homework........20%
Project I.......20%
Project II......30%
Presentation....30%

Computer

Homework and projects will require access to a fast personal computer to MATLAB or equivalent. Familiarity with Matlab or another simulation language is essential


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