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Master AI techniques for video games including pathfinding, procedural generation, movement systems, and real-time strategy AI through hands-on projects and expert insights.
Explore EntityManager implementation, 2D game math concepts, and C++ programming techniques for game development. Learn about data separation, vector operations, and collision detection.
Explore game programming concepts, including SFML graphics, entity-component systems, and game architecture. Learn to implement player controls, enemies, scoring, and special abilities.
Explore Entity Component System (ECS) architecture for game programming, covering design principles, implementation details, and practical coding examples using C++ and SFML.
Explore SFML and ImGui for game programming, covering Makefiles, widgets, windows, events, drawing, sprites, and transformations. Learn practical skills for creating interactive graphics.
Learn essential research and programming practices for computer science grad school, from version control to experiment organization and data visualization.
Learn game programming fundamentals, including vector math, rendering, AI, and physics. Set up C++ environment and understand assignment structure for creating functional games using ECS architecture and SFML library.
Master C++ fundamentals, from basic syntax to advanced concepts like memory management, pointers, and object-oriented programming. Includes live coding sessions for practical application.
Explore game programming fundamentals, including vector math, rendering, AI, physics, and UI. Learn to create functional games using ECS architecture, C++, and SFML graphics library.
Explore deep neural networks, their architecture, and applications in AI. Learn key concepts and techniques for implementing advanced machine learning models.
Explore fundamental concepts of neural networks, their structure, and applications in AI problem-solving environments.
Explore temporal difference learning and its applications in AI, focusing on practical implementations and problem-solving techniques.
Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.
Explore Markov Decision Processes (MDPs) in AI, learning algorithmic techniques for modern problem-solving in uncertain environments.
Explore bandit algorithms in AI, learning key techniques for optimizing decision-making in uncertain environments through practical applications and examples.
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