CoE 161

• Introduction to Information and Complexity (2018 Curriculum)
  • Advanced course on information theory and computational complexity, starting from Shannon's information theory and Turing's theory of computation, leading to the theory of Kolmogorov complexity.

• Semester Offered: 2nd semester
• Course Credit: Lecture: 3 units

Prerequisites

• EEE 111 (Introduction to Programming and Computation)
• EEE 137 (Probability, Statistics and Random Processes in Electrical and Electronics Engineering)

Course Goal

• Introduce fundamental tools and frameworks to understand information and complexity in the design of computer systems.

Specific Goals

• Introduce fundamental tools for determining the minimum amount of computational resources needed to algorithmically solve a problem.
  • Information Theory
  • Computational Complexity Theory

Content

This course covers information theory and computational complexity in a unified way. It develops the subject from first principles, building up from the basic premise of information to Shannon's information theory, and from the basic premise of computation to Turing's theory of computation. The duality between the two theories leads naturally to the theory of Kolmogorov complexity. The technical topics covered include source coding, channel coding, rate-distortion theory, Turing machines, computability, computational complexity, and algorithmic entropy, as well as specialized topics and projects.