Markov Decision Processes: Foundations of Reinforcement Learning

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Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Covers decision-making in marketing based on customer behavior for optimal strategies.

Covers the process of solving Linear Programs (LPs) using the simplex method.

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.

Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.