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This version was saved 15 years, 9 months ago View current version     Page history
Saved by olivier
on September 21, 2006 at 11:41:41 am

Mathematical Foundations of Learning Theory


Machine Learning relates to many different domains: data analysis, data processing, information retrieval, statistics, knowledge discovery, reasoning under uncertainty...


But the main distinguishing feature of the learning problem is that it requires a form of reasoning which is non-deductive: it requires induction, transduction, abduction or generalization.


As a result, the learning problem has its roots in the following theories





Another similar list (to be merged with the previous one)


  • Formalizing reasoning / Foundational issues
    • Logic W
    • Induction Theory W, Philosophy of induction


  • Formalizing decision-making and interactions
    • Decision Theory W
    • Auction Theory W
    • Rational Choice Theory, Public choice theory, Social choice theory...
    • Portfolio Theory W, Sharpe ratio W
    • Theory of Risk Aversion W, Utility W
    • Game Theory W, W
    • Finance, Gambling Theory W, Arbitrage W, Prediction Markets W, Efficient market theory W, Value-at-Risk W, Hedge W
    • Interpretation of Probability W


  • Formalizing information and regularity
    • Information Theory W
    • Algorithmic Information Theory W
    • Compression W, Universal compression
    • Randomness W


  • Formalizing Inference
    • Logic Inference
    • Inductive Inference
    • Statistical Inference


  • Tools
    • Probability Theory
    • Optimization Theory
    • Algorithms
    • Functional Analysis

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