Machine Learning

Introduction: Overview of machine learning, Machine learning applications and examples, inductive learning, inductive bias; Decision tree learning: Representation, algorithm, hypothesis space, rule extraction, pruning; Neural networks: Representation, perceptrons, multilayer networks, backpropagation algorithm, Training procedures; Bayesian learning: Bayes theorem, maximum likelihood hypothesis, Bayes classifiers, Baysian belief networks; Instance-based learning: K-nearest neighbours, lazy and eager learning; Evaluating Hypothesis: Hypothesis accuracy, sampling theory, hypothesis testing; Computational learning theory: Probably learning an approximately correct hypothesis, sample complexity for finite and infinite hypothesis, VC dimension; Unsupervised learning: Clustering, types, steps