Introduction: Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning.

Concept Learning and the General-to-Specific Ordering: A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate-Elimination Algorithm, Remarks on Version Spaces and Candidate-Elimination, Inductive Bias.

Learning Sets of Rules: Sequential Covering Algorithms, Learning Rule Sets: Example-Based Methods, Learning First-Order Rules, FOIL: A First-Order Inductive Learner.

Analytical Learning: Perfect Domain Theories: Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge, Inductive-Analytical Approaches to Learning.

Decision by Committee: Ensemble Learning: Boosting: Adaboost , Stumping, Bagging: Subagging, Random Forests, Comparison With Boosting, Different Ways To Combine Classifiers.

Unsupervised Learning: The K-MEANS algorithm : Dealing with Noise ,The k-Means Neural Network , Normalisation ,A Better Weight Update Rule ,Using Competitive Learning for Clustering.

Unsupervised Learning: Vector Quantisation, the self-organising feature map , The SOM Algorithm, Neighbourhood Connections, Self-Organisation, Network Dimensionality and Boundary Conditions, Examples of Using the SOM.

Markov Chain Monte Carlo (MCMC) Methods: Sampling Random Numbers ,Gaussian Random Numbers ,Monte Carlo Or Bust ,The Proposal Distribution , Markov Chain Monte Carlo.

Graphical Models: Bayesian Network, Approximate Inference, Making Bayesian Networks, Markov Random Fields, Hidden Markov Models (Hmms), The Forward Algorithm, The Viterbi Algorithm, The Baum-Welch Or Forward-Backward Algorithm, Tracking Methods, The Kalman Filter, The Particle Filter.