Learning Problems, Designing Learning systems, Perspectives and Issues, Concept Learning, Version Spaces and Candidate Elimination Algorithm, Inductive bias, Decision Tree learning Algorithm, Heuristic Space Search.

Neural Network Representation, Problems, Perceptions, Multilayer Networks and Block Algorithms, Genetic Algorithms, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning.

Bayes Theorem, Concept Learning, Maximum Likelihood, Minimum Description Length Principle, Bayes Optimal Classifier, Gibbs Algorithm, Naive Bayes Classifier Bayesian Belief Network, EM(Expectation-Maximization) Algorithm, Probably Learning, Sample Complexity for Finite and Infinite Hypothesis Spaces, Mistake Bound Model.

k-Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Sequential Covering Algorithms, Learning Rule Sets, Learning Sets of First Order Rules, Induction as Inverted Deduction, Inverting Resolution.

Perfect Domain Theories, Explanation Based Learning, Inductive Analytical Approaches, FOCL (First Order Combined Learner) Algorithm, Reinforcement Learning, Task, Q-Learning, Temporal Difference Learning.