Machine learning, game theory and markov chains
Objectives

Understand the details of linear programming problems and their dual versions.

Study the applications of linear programming to data science and signal processing problems.

Develop the details of game theory necessary to understand the balance of nash in zerosum games and its relationship to linear programming.

Motivate the study of markov chains and their use in machine learning.
Syllabus
1. Linear programming


Detailed approach and mathematical review

Classic applications

Applications to data science

Comparison with the perceptron

Dual programming

2. Game theory


Zero sum games

Nash equilibria

Mixed strategies

Applications of linear programming and von Neumann's theorem

3. Invitation to the Markov chains


Stochastic game theory

Formal definitions and probability review

Law of the big numbers

Time series

Sampling methods

Ergodicity theorems

Relationship with reinforcement learning
