Abstract
This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications and Machine Learning.

Educational Course Subject Areas
Artificial Intelligence

Keywords
Information theoretic learning ,  Nonparameteric entropy estimation ,  Information potential ,  Information forces ,  Stochastic Information Gradient ,  Kernel Annealing ,  Euclidean and Cauchy Schwartz pdf Distances ,  Mermaid algorithm