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This is a graduate-level introduction to mathematics of information theory. The aim of this course is to cover both classical and modern topics. In addition to that, it includes information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression. If you are looking for knowledge about Information theory, then this course is the right course for you.

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: MIT

Course Curriculum

Information Measures
Information measures Entropy and divergence 00:15:00
Information measures Mutual information 00:10:00
Sufficient statistic. Continuity of divergence and mutual information 00:00:00
Extremization of mutual information Capacity saddle point 00:10:00
Single-letterization. Probability of error. Entropy rate 00:10:00
Lossless Data Compression
Variable-length Lossless Compression 00:15:00
Fixed-length (almost lossless) compression. Slepian-Wolf problem 00:10:00
Compressing stationary ergodic sources 00:10:00
Universal compression 00:10:00
Binary Hypothesis Testing
Binary hypothesis testing 00:10:00
Hypothesis testing asymptotics I 00:10:00
Information projection and Large deviation 00:10:00
Hypothesis testing asymptotics II 00:10:00
Channel Coding
Channel coding 00:10:00
Channel coding Achievability bounds 00:10:00
Linear codes. Channel capacity 00:10:00
Channels with input constraints. Gaussian channels 00:10:00
Lattice codes (by O. Ordentlich) 00:10:00
Channel coding Energy-per-bit, continuous-time channels 00:15:00
Advanced channel coding. Source-Channel separation 00:10:00
Channel coding with feedback 00:15:00
Capacity-achieving codes via Forney concatenation 00:10:00
Lossy Data Compression
Rate-distortion theory 00:10:00
Rate distortion Achievability bounds 00:10:00
Evaluating R(D). Lossy Source-Channel separation 00:10:00
Advanced Topics
Multiple-access channel 00:10:00
Examples of MACs. Maximal Pe and zero-error capacity 00:10:00
Random number generators 00:10:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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