UNIT I ?????????? Introduction
Learning Problems ? Perspectives and Issues ? Concept Learning ? Version Spaces and Candidate Eliminations ? Inductive bias ? Decision Tree learning ? Representation ? Algorithm ? Heuristic Space Search. (Chapter – 1)
UNIT II ????????? Neural Networks and Genetic Algorithms
Neural Network Representation ? Problems ? Perceptrons ? Multilayer Networks and Back Propagation Algorithms ? Advanced Topics ? Genetic Algorithms ? Hypothesis Space Search ? Genetic Programming ? Models of Evaluation and Learning. (Chapter – 2)
UNIT III ???????? Bayesian and Computational Learning
Bayes Theorem ? Concept Learning ? Maximum Likelihood ? Minimum Description Length Principle ? Bayes Optimal Classifier ? Gibbs Algorithm ? Na?ve Bayes Classifier ? Bayesian Belief Network ? EM Algorithm ? Probability Learning ? Sample Complexity ? Finite and Infinite Hypothesis Spaces ? Mistake Bound Model. (Chapter – 3)
UNIT IV ???????? Instant Based Learning
K- Nearest Neighbour Learning ? Locally weighted Regression ? Radial Basis Functions ? Case Based Learning. (Chapter – 4)
UNIT V ????????? Advanced Learning
Learning Sets of Rules ? Sequential Covering Algorithm ? Learning Rule Set ? First Order Rules ? Sets of First Order Rules ? Induction on Inverted Deduction ? Inverting Resolution ? Analytical Learning ? Perfect Domain Theories ? Explanation Base Learning ? FOCL Algorithm ? Reinforcement Learning ? Task ? Q-Learning ? Temporal Difference Learning (Chapter – 5)