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

Explores solving Connect Four using game theory algorithms to find optimal strategies efficiently.

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 and algorithms for optimal strategy in minimum time.

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

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

Chemical Reaction Optimization: Multi-Task Learning

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

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

MILP Model and Typical Days by FM

Discusses MILP model, typical days, clustering, and extreme periods analysis in energy systems optimization.

Optimization Methods: Theory Discussion

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

Motor control systems

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

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.

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

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

Computational Aspects of Optimization

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

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.

Two Stage Heat Pump: Application and Optimization

Covers the application and optimization of a two-stage heat pump system, emphasizing accurate modeling and optimization for energy systems.

Markov Games: Concepts and Applications in Reinforcement Learning

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

Multistage Games: Extensive Form and Feedback Strategies

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

Energy optimization strategies

Covers brainstorming options for smart operation changes, heat recovery, and PV panel performance.

Image Processing Basics

Covers the basics of image processing, focusing on writing a program to process images.