Introduction to machine learning: Designing a learning system, perspective issues in machine learning, supervised and unsupervised learning
Concept learning and the general to specific Ordering: induction learning hypothesis, Find-S, version space and candidate elimination algorithms, List-then-elimination algorithms, A biased hypothesis space, unbiased hypothesis space, decision tree
Artificial Neural networks: Multilayer networks and back propagation algorithms, Recurrent network, Evaluation hypothesis
Bayesian learning: Naive bays classifier, Gibbs algorithms, Bayesian belief Networks, EM algorithms, Computational learning theory, probability learning theory, sample complexity finite hypothesis space, sample complexity infinite hypothesis space, Mistake bound model of learning, Instance based learning, K-nearest neighbor learning
Genetic algorithms, Learning set rules, Analytical learning, Combining inductive and Analytical learning, Reinforcement learning, SVM, Boasting, Clustering, training and testing, cross validation, prediction performance.