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.
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)
