Machine Learning 2 Syllabus

Machine Learning 

UNIT - I

Introduction- Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning.

Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm.

 UNIT - II

Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.

Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.

 UNIT - III

Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.

Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.

 UNIT -IV

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL    

UNIT - V

Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.

Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

 Text Books:

1.     Machine Learning – Tom M. Mitchell, – MGH

2.     Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

 Reference Books:

1.  Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ. Press.

2.   Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.