Practical Introduction to Information Theory

Partner: Udemy
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Description: Section 1 IntroductionInformation theory is also called ‘the method of maximum entropy’. Here the title ‘information theory’ is preferred as it is more consistent with the course’s objective – that is to provide plausible deductions based on available information.Information theory is a relatively new branch of applied and probabilistic mathematics – and is relevant to any problem that can be described as a probability problem.Hence there are two primary skills taught in this course.1. How to formulate problems as probability problems.2. How to then solve those problems using information theory.Section 1 largely focuses on the probabilistic basis of information theory.Lecture 1 Scope of CourseIn this lecture we discuss the scope of the course. Whilst section 1 provides the probabilistic basis of information theory the following sections are:2. The thermodynamic perspective3. Mineral Processing Applications4. Other examples5. Games and puzzles6. CloseInformation theory is a foundational approach to a particular approach of machine learning. The scope of this course does not go deeply into machine learning but does provide some insight into how information theory is the basis of machine learning.Lecture 2 Combinatorics as a basis for Probability TheoryThis course does not aim to be a probability course – and only provides a probability perspective relevant to information theory.In this lecture you will learn basic probability notation and formula.By applying the exercises you will develop understanding of the fundamental functions such as factorial and combinatorial.Lecture 3 Probability TheoryIn this lecture you will learn basic probability theory with primary focus being the binomial distribution. Here probability theory is based on first starting with single-event
Category: Teaching & Academics > Engineering > Probability
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Price: 29.99
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