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

Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3

Module 5 PPT

Module 5 Notes

Module 5 QB