Abstract
Niv/tomr - Learning Chess from data Is watching a chess game enough to figure out the rules? What is common denominator between different plays and game ending? In this presentation, we will show how Machine Learning and Hadoop can help us re-discover chess rules and gain new understanding of the game. ----- Can empirical samples unveil the big picture? Is chess games descriptions expose good enough data to gain understanding of chess rules - legal piece moves, castling, check versus checkmate, etc. Which features are important in describing a chess game and which features are not. What is a good representation of a chess game for this uses. What is the minimal sample size which is required in order to learn this in a good enough manner and where this learning can go wrong. **Ne3 => E=mc2** Looking at the bigger picture - Can we understand big systems based on empirical samples. Can we reverse engineer physics and discover how physical system work based on no external knowledge beside empirical samples.
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