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Computer Science
Artificial Intelligence
Deep Learning
Reinforcement Learning
1. Foundations of Reinforcement Learning
2. Mathematical Foundations
3. Markov Decision Processes
4. Dynamic Programming
5. Monte Carlo Methods
6. Temporal-Difference Learning
7. Function Approximation
8. Deep Reinforcement Learning
9. Policy Gradient Methods
10. Advanced Topics
11. Implementation and Practical Considerations
12. Applications and Case Studies
2.
Mathematical Foundations
2.1.
Probability Theory Essentials
2.1.1.
Random Variables
2.1.2.
Probability Distributions
2.1.3.
Expectation and Variance
2.1.4.
Conditional Probability
2.2.
Optimization Fundamentals
2.2.1.
Gradient Descent
2.2.2.
Stochastic Optimization
2.2.3.
Convex vs Non-convex Problems
2.3.
Linear Algebra Basics
2.3.1.
Vectors and Matrices
2.3.2.
Matrix Operations
2.3.3.
Eigenvalues and Eigenvectors
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1. Foundations of Reinforcement Learning
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3. Markov Decision Processes