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.

Text Book 1 : Ch 1 & 2

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.

Text Book 1 : Ch 10 & 11

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.

Text Book 2: Chap 13 and 14.1

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.

Text Book 2: Chap 14.2, 14.3, 15

Graphical Models: Bayesian Networks: 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.

Text Book 2 : Chap 16