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Learn about key concepts in probability and information theory through a comprehensive graduate-level lecture covering exponential families, normalizers, and the multivariable Gaussian distribution. Explore the mathematical foundations of exponential families, including their properties and components, while diving into the significance of normalizer Z and its gradient. Gain insights into information theory principles and their applications in data science, with detailed explanations of mathematical concepts and their practical implications.
Syllabus
Introduction
Exponential Family
Exponents
Normalizer
Multivariable Gaussian Distribution
Normalizer Z
Gradient
Information Theory
Taught by
UofU Data Science