Syllabus

UNIT:1  

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

Analytical learning and reinforced learning: Perfect Domain Theories, Explanation Based Learning, Inductive, Analytical Approaches, FOCL Algorithm, Reinforcement Learning, Task – Q-Learning, Temporal Difference Learning 

UNIT:2      

Neural networks and genetic algorithms: Neural Network Representation – Problems – Perceptron – Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. 

UNIT:3

Bayesian and computational learning: Bayes Theorem, Concept Learning, Maximum Likelihood, Minimum Description Length Principle, Bayes Optimal Classifier, Gibbs Algorithm, Naïve Bayes Classifier, Bayesian Belief Network , EM Algorithm , Probably Learning , Sample Complexity for Finite and Infinite Hypothesis Spaces, Mistake Bound Model.

UNIT:4 

Instant based learning and learning set of rules: K- Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Sequential Covering Algorithms, Learning Rule Sets, Learning First Order Rules, Learning Sets of First Order Rule, Induction as Inverted Deduction, Inverting Resolution.