InfoCoBuild

An Introduction to Information Theory

An Introduction to Information Theory. Instructor: Prof. Adrish Banerjee, Department of Electrical Engineering, IIT Kanpur. Information Theory answers two fundamental questions: what is the maximum data rate at which we can transmit over a communication link, and what is the fundamental limit of data compression. In this course we will explore answers to these two questions. We will study some practical source compression algorithms. We will also study how to compute channel capacity of simple channels. (from nptel.ac.in)

Lecture 04 - Problem Solving Session I

In this lecture we solve some problems related to properties of conditional entropy, mutual information, and divergence.

Go to the Course Home or watch other lectures:

Lecture 01 - Introduction

Lecture 02 - Measure Information

Lecture 03 - Information Inequalities

Lecture 04 - Problem Solving Session I

Lecture 05 - Block to Variable Length Coding: Prefix Free Code

Lecture 06 - Block to Variable Length Coding: Bounds on Optimal Code Length

Lecture 07 - Block to Variable Length Coding: Huffman Coding

Lecture 08 - Variable to Block Length Coding

Lecture 09 - The Asymptotic Equipartition Property

Lecture 10 - Block to Block Coding of DMS (Discrete Memoryless Source)

Lecture 11 - Problem Solving Session II

Lecture 12 - Universal Source Coding: Lempel-Ziv Algorithm - LZ77

Lecture 13 - Universal Source Coding: Lempel-Ziv Welch Algorithm (LZW)

Lecture 14 - Coding of Sources with Memory

Lecture 15 - Channel Capacity

Lecture 16 - Jointly Typical Sequences

Lecture 17 - Noisy Channel Coding Theorem

Lecture 18 - Differential Entropy

Lecture 19 - Gaussian Channel

Lecture 20 - Parallel Gaussian Channel

Lecture 21 - Problem Solving Session III

Lecture 22 - Rate Distortion Theory

Lecture 23 - Blahut-Arimoto Algorithm

Lecture 24 - Problem Solving Session IV