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Machine learning, game theory and markov chains

Objectives

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

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

  3. Develop the details of game theory necessary to understand the balance of nash in zero-sum games and its relationship to linear programming.

  4. 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

Contact

CDMX, México.

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