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Machine Learning
Mini-chess AI
Decision tree model to predict optimal chess moves, implementing machine learning algorithms. Focused on improving basic Machine Learning concepts and building ML models using C++.
Overview
An AI chess engine that uses decision tree algorithms to evaluate board positions and predict optimal moves.
Technologies
C++Machine LearningDecision TreesAlgorithmsData Structures
The Challenges
- Implementing efficient decision tree algorithms
- Optimizing move evaluation functions
- Handling large search spaces
- Balancing accuracy vs performance
The Solutions
- Used alpha-beta pruning to reduce search space
- Implemented heuristic evaluation functions
- Created efficient data structures for board representation
- Applied machine learning techniques for move prediction
Key Outcomes
- Developed a functional chess AI engine
- Improved understanding of ML algorithms
- Enhanced C++ programming skills
- Gained experience with game AI development