Module - 1
Introduction : Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning.
Concept Learning : Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias. (Chapter - 1)
Module - 2
Decision Tree Learning : Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning. (Chapter - 2)
Module - 3
Artificial Neural Networks : Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm. (Chapter - 3)
Module - 4
Bayesian Learning : Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm. (Chapter - 4)
Module - 5
Evaluating Hypothesis : Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypothesis, Comparing learning algorithms.
Instance Based Learning : Introduction, k-nearest neighbor learning, locally weighted regression, radial basis function, cased-based reasoning.
Reinforcement Learning : Introduction, Learning Task, Q Learning. (Chapter - 5)