Overview  
Administrivia, Learning  Supervised,
Unsupervised, Other 
Introduction 
Bishop Ch.1 
Background: Probability & Statistics, Gaussians, Detection Theory, Information Theory 
Linear Models for Regression  Bishop Ch.3 
Linear basis function models; biasvariance; Bayesian linear regresson 
Linear Models for Classification 
Bishop Ch.4 
Discriminant functions; Probabilistic Generative Models; Probabilistic Discriminative Models 
Decision Trees 
Mitchell Ch.3 
Decision tree learning algorithm; bias issues 
Neural Networks 
Bishop Ch.5 
Feedforward networks; Error back propagation;
Regularization 
Large Margin Machines 
Bishop Ch.7 
Maximum margin machines; support vector
machines; relevance vector machines 
Perceptron Algorithm 
Article 
Perceptron algorithm; application to sequence processing (tagging); feature selection 
Kernel Methods 
Bishop Ch.6  Dual
representations; constructing kernels; radial basis
functions 
Feature
Selection/Reduction  Bishop Ch.12 
Features transformations; PCA; kernel PCA; ICA;
other feature selection strategies 
Reinforcement
Learning  Mitchell Ch.13 
Learning task; MDPs; Q learning
more slides

Graphical
Models  Bishop Ch.8 
Probabilisitic graphical models 