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
- Decision Theory
- Probability Theory
- Statistics
- Estimation Theory
- Approximation Theory
- Game Theory
- Inductive Inference
- Computation Theory
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
Comments (0)
You don't have permission to comment on this page.