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

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Variance Reduction: Strategies and Applications

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Optimization and Simulation: Heuristics Intensification

Explores optimization and simulation techniques, emphasizing heuristics intensification for efficient solution finding.

Markov Games: Concepts and Applications in Reinforcement Learning

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

Optimization Methods

Covers the Newton's local method in Python using NumPy for optimization.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Shape Design: Optimization and Simulation

Explores shape design, optimization, and simulation using geodesic CNNs for aerodynamic performance.

Monte Carlo Tree Search and Alpha Zero

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Simplex Algorithm: Basics

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Optimisation in Energy Systems

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

Introduction to Computer Science: Three Domains

Covers scientific computing, process automation, and data management in computer science applications across different sectors.