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Information theory is the mathematical theory that deals with the fundamental aspects of communication systems. This [course_title] is meant to serve as an introduction to some basic concepts in information theory and error-correcting codes, and some of their applications in computer science and statistics. It covers the basics of information theory like the information associated with an event, entropy, mutual information, data processing theorem, source coding, Huffman codes, channel capacity and the channel coding theorem.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: Open Culture 

Course Curriculum

module :1
Mod-01 Lec-01 Introduction to Information Theory and Coding 00:53:00
Mod01 Lec-02 Definition of Information Measure and Entropy 00:53:00
Mod-01 Lec-03 Extension of An Information Source and Markov Source 00:56:00
Mod-01 Lec-04 Adjoint of An Information Source, Joint and Conditional Information Measures 00:57:00
Mod-01 Lec-05 Properties of Joint and Conditional Information Measures and a Markov Source 00:50:00
Mod-01 Lec-06 Asymptotic Properties of Entropy and Problem Solving in Entropy 00:56:00
Mod-01 Lec-07 Block Code and Its Properties 00:51:00
Mod-01 Lec-08 Instantaneous Code and Its Properties 00:52:00
Mod-01 Lec-09 Kraft-Mcmillan Equality and Compact Codes 00:52:00
Mod-01 Lec-10 Shannon`s First Theorem 00:52:00
module : 2
Mod-01 Lec-11 Coding Strategies and Introduction to Huffman Coding 00:54:00
Mod-01 Lec-12 Huffman Coding and Proof of Its Optimality 00:54:00
Mod-01 Lec-13 Competitive Optimality of The Shannon Code 00:51:00
Mod-01 Lec-14 Non-Binary Huffman Code and Other Codes 00:48:00
Mod-01 Lec-15 Adaptive Huffman Coding part-1 00:51:00
Mod-01 lec-16 Adaptive Huffman Coding Part-2 00:49:00
Mod-01 Lec-17 Shannon-Fano-Elias Coding and Introduction to Arithmetic Coding 00:53:00
Mod-01 Lec-18 Arithmetic Coding Part-1 00:50:00
Mod-01 Lec-19 Arithmetic Coding Part-2 00:52:00
Mod-01 Lec-20 Introduction to Information Channel 00:56:00
module : 3
Mod-01 Lec-21 Equivocation and Mutual Information 00:52:00
Mod-01 Lec22 Properties of Different Information Channels 00:54:00
Mod-01 Lec-23 Reduction of Information Channels 00:51:00
Mod-01 Lec-24 Properties of Mutual Information and Introduction to Channel Capacity 00:52:00
Mod-01 Lec-25 Calculation of Channel Capacity for Different Information Channel 00:47:00
Mod-01 Lec-26 Shannon`s Second Theorem 00:50:00
Mod-01 Lec-27 Discussion on Error Free Communication Over Noisy Channel 00:53:00
Mod-01 Lec-28 Error Free Communication Over a Binary Symmetric Channel 00:50:00
Mod-01 Lec-29 Differential Entropy and Evaluation of Mutual Information 00:16:00
Mod-01 Lec-30 Channel Capacity of a Bandlimited Continuous Channel 00:56:00
module : 4
Mod-01 Lec-31 Introduction to Rate-Distortion Theory 00:49:00
Mod-01 Lec-32 Definition and Properties of Rate-Distortion Functions 00:47:00
Mod-01 Lec-33 Calculation of Rate-Distortion Functions 00:54:00
Mod-01 Lec-34 Computational Approach For Calculation of Rate-Distortion Functions 00:49:00
Mod-01 Lec-35 Introduction to Quantization 00:51:00
Mod-01 Lec-36 Lloyd-Max Quantizer 00:49:00
Mod-01 Lec-37 Companded Quantization 00:58:00
Mod-01 Lec-38 Variable Length Coding and Problem Solving In Quantizer Design 00:51:00
Mod-01 Lec-39 Vector Quantization 00:55:00
Mod01 Lec-40 Transform Part-1 00:52:00
Mod-01 Lec-41 Transform Coding Part-2 00:54:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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