Lectures related to Connect Four: Game Theory Approach

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory and algorithms optimization, comparing minimax, alpha-beta pruning, and Monte-Carlo tree search.

Connect Four: Alpha-Beta Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory algorithms and compares Alpha-Beta pruning with Monte-Carlo tree search.

Connect Four: Alpha-Beta Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory algorithms and compares their performance.

Connect Four: Alpha-Beta Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory and algorithms for optimal strategy in minimum time.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores solving Connect Four using game theory algorithms and compares their efficiency.

Solving Connect Four: A-B Pruning and Monte-Carlo Tree Search

Explores applying game theory to optimize strategies in Connect Four using advanced algorithms.

Connect Four: α-β Pruning vs Monte-Carlo Tree Search

Explores strategies to solve Connect Four using α-β pruning and Monte-Carlo tree search.

Solving Connect Four: Game Theory Strategies

Explores game theory strategies to solve Connect Four efficiently using minimax, alpha-beta pruning, and Monte Carlo methods.

Markov Games: Concepts and Applications in Reinforcement Learning

Covers Markov games, their dynamics, equilibria, and applications in reinforcement learning.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Introduction to Game Theory

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

Multistage Games: Extensive Form and Feedback Strategies

Explores multistage games, focusing on extensive form and feedback strategies, including Nash equilibria and backward induction methods.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Motor control systems

Explores motor control systems, covering algorithms, sensor integration, and practical applications in robotics and automation.

Optimisation in Energy Systems

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

Potential Games: Best-Response Dynamics and Equilibria

Covers potential games, their properties, and the convergence of best-response dynamics to Nash equilibria.

Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Zero-Sum Games: Concepts and Applications

Covers zero-sum games, focusing on Nash equilibria, security strategies, and their applications in various scenarios.