Introduction

• Introductory Lecture   (duration 24:50)

Lesson 1 – Sequence Spaces, Random Variables, and Probabilistic Processes

• Lesson 1-1: Strings (duration 7:34, slides)
• Lesson 1-2: Sequence Spaces (duration 14:16, slides)
• Lesson 1-3: Infinite Paths through Trees (duration 13:06, slides)
• Lesson 1-4: Measure Spaces (duration 18:59, slides)
• Lesson 1-5: Measures on Sequence Spaces (duration 15:55, slides)
• Lesson 1-6: Random Variables and Stochastic Processes (duration 21:34, slides)

Lesson 2 – Computability

• Lesson 2-1: Finite Automata (duration 16:19, slides)
• Lesson 2-2: Turing Machines (duration 17:37, slides)
• Lesson 2-3: Turing Machines – an example (duration 15:20, slides)
• Lesson 2-4: Computable Functions (duration 19:55, slides)
• Lesson 2-5: The Halting Problem (duration 20:30, slides)
• Lesson 2-6: Undecidability of the Halting Problem (duration 12:50, slides)

Lesson 3 – Dynamical Systems

• Lesson 3-1: Subshifts (duration 16:44, slides)
• Lesson 3-2: Measurable Dynamics (duration 24:54, slides)
• Lesson 3-3: Markov Shifts (duration 30:33, slides)
• Lesson 3-4: Markov Chains (duration 20:17, slides)
• Lesson 3-5: Ergodicity (duration 32:29, slides)
• Lesson 3-6: The Ergodic Theorem (duration 46:38, slides)

Lesson 4 – Entropy

• Lesson 4-1: Information Measures (duration 16:05, slides)
• Lesson 4-2: The Definition of Entropy (duration 23:00, slides)
• Lesson 4-3: Kolmogorov Complexity (duration 36:32, slides)
• Lesson 4-4: Prefix-free Complexity (duration 29:14, slides)
• Lesson 4-5: Mutual Information (duration 35:30, slides)

Lesson 5 – Coding

• Lesson 5-1: Optimal Codes (duration 35:00, slides)