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Syllabus

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A. Mathematical foundations

1. Stochastic Gradient Descent

2. Markov chains

3. Markov Decision processes

4. Dynamic programming

5. Bellman's equations

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B. Monte Carlo Method and Machine Learning

1. Monte Carlo prediction

2. Monte Carlo Control
3. Monte Carlo methods and Markov chains: application to the location of robots.

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Reinforcement Learning

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A. Introduction: reward based problems

B. Return, Policy and Value Functions

C. Tabular Methods
D. Applications

E. Deep and Reinforcement Learning

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