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Lecture
Controlled Stochastic Processes
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Controlled Stochastic Processes
Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.
Asset Selling: Optimal Revenue Policy
Explores asset selling dynamics, optimal revenue policy, acceptance thresholds, and commodity price impact.
Markov Decision Processes: Foundations of Reinforcement Learning
Covers Markov Decision Processes, their structure, and their role in reinforcement learning.
Optimal Stopping Problems: Theory and Applications
Covers optimal stopping problems in applied probability and stochastic processes, focusing on theory and practical applications.
Dynamic Programming: Optimal Control
Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.
Optimal Distributed Control: Projected GD for Locally Optimal Controllers
Covers optimal distributed control using Gradient Descent to achieve locally optimal controllers in large-scale systems.
The Marriage Problem
Explores the marriage problem, modeling the process as a controlled stochastic process with dynamic programming algorithms to find the optimal policy for accepting bachelors.
Infinite-Horizon Problems: Formulation & Complexity
Covers infinite-horizon problems in Applied Probability and Stochastic Processes.
Infinite-Horizon LQ Control: Solution & Example
Explores Infinite-Horizon Linear Quadratic (LQ) optimal control, emphasizing solution methods and practical examples.
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
