The course gives a basic introduction to information theory and channel coding with applications in statistics and digital communications.
Central concepts: entropy, mutual information, asymptotic equipartition principle and entropy for stochastic processes, data compression, source coding, channel capacity, channel coding, capacity for specific channel models with focus on discrete and Gaussian models, finite field theory, analysis and design of algebraic channel codes and network theory.
Formats: The course is presented in a series of eight seminars.
After passing the course, the student shall be able to
- give an introduction to the historical development and importance for the modern society of the subject
- explain the basic principles and theoretical concepts that form the basis for information theory
- formulate a mathematical model that is applicable and relevant for a given problem in the area
- use a given or individually formulated mathematical model for solving a given technical problem in the area and analyse the result and its reasonableness
- compare different algorithms and encoding techniques, put different techniques against one another and assess the suitability of individual techniques in different situations
- account for how information theoretical upper and lower bounds are formulated and proven.