Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.
Explores the marriage problem, modeling the process as a controlled stochastic process with dynamic programming algorithms to find the optimal policy for accepting bachelors.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Discusses algorithms for balancing exploration and exploitation in decision-making processes.
Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
