Tentative Teaching Syllabus (note: this is only a very tentative entry that is still under review and evolving)

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

• Instance based Learning
  - k-Nearest Neighbor
  - Radial basis functions
  - Case-based 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

• Ensemble Clsssification
  

• Artificial Neural Nets
  - perceptrons
  - multilayer networks
  - backpropagation
-self-organizing feature maps -evolving neural networks

• Evaluating Hypotheses

• Computational Learning
  - PAC learning
  - the VC dimension

• Bayesian Learning
  - Bayes Theorem
  - Bayes Optimal
  Classifier

• Reinforcement Learning
  - Q learnin

• Rule based learning