 Introduction
 What is machine
learning?
 Why machine learning?
 Classification and machine learn?

 Instance based Learning
 kNearest Neighbor
 Radial basis functions
 Casebased reasoning

 Concept Learning
 concept space,
instance space,
hypothesis space, ...
 inductive bias

 Decision Tree Learning
 representation
 ID3
 C4.5

 Ensemble Learning
 Bagging,
Boosting,
Classifer dependency, ...
 inductive bias


 Artificial Neural Nets
 perceptrons
 multilayer networks
 backpropagation
selforganizing feature maps
evolving neural networks


 Computational Learning
 PAC learning
 the VC dimension

 Bayesian Learning
 Bayes Theorem
 Bayes Optimal
Classifier

 Reinforcement Learning
 Q learnin

