Lectures related to Dynamic programming

Delves into dynamic programming with a focus on Matrix Chain Multiplication and the longest common subsequence problem.

Dynamic Programming: Rod Cutting and Change Making

Explores dynamic programming through rod cutting and change making optimization problems.

Dynamic Programming: Longest Common Subsequence

Explores dynamic programming with a focus on the Longest Common Subsequence problem and its efficient solutions.

Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.

Longest Common Subsequence: Dynamic Programming Algorithm

Explores the Longest Common Subsequence concept and its dynamic programming algorithm, emphasizing optimal substructure and efficient problem-solving.

Asset Selling: Optimal Revenue Policy

Explores asset selling dynamics, optimal revenue policy, acceptance thresholds, and commodity price impact.

Asset Selling Problem

Explores the Asset Selling Problem to maximize long-term reward without a deadline.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Covers dynamic programming techniques for solving the rod cutting and matrix chain multiplication problems.

Linear Programming Techniques in Reinforcement Learning

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Introduces dynamic programming with a focus on rod cutting and matrix chain multiplication.

Dynamic Programming: Fibonacci Numbers

Explores dynamic programming through Fibonacci numbers, memoization, and rod cutting applications.

Dynamic Programming: Optimal Control

Explores Dynamic Programming for optimal control, covering machine replacement, Markov chains, control policies, and linear quadratic problems.

Interactive Lecture: Reinforcement Learning

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

Dynamic Programming: Knapsack

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Asset Selling Problems

Discusses asset selling problems and optimal selling policies based on market conditions and termination states.

Dynamic Programming Recap

Covers a recap of dynamic programming and optimal betting strategy in a gambling problem.

Dynamic Programming: Matrix-chain Multiplication

Explores dynamic programming with a focus on matrix-chain multiplication and the importance of optimal substructure.

Controlled Stochastic Processes

Explores controlled stochastic processes, dynamic programming, and the Machine Replacement Problem.