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CS8082 MACHINE LEARNING TECHNIQUES Lecture Notes Download CS8082 MACHINE LEARNING TECHNIQUES Lecture Notes Download
Department  Electronics Communication Engineering
Year 4th Year
Semester 7th Semester
University Anna University
Regulation  R2017
Subject Code CS8082
Subject Name MACHINE LEARNING TECHNIQUES
Download File CS8082 MACHINE LEARNING TECHNIQUES Lecture Notes, Books, Important 2 Marks Question and Answers, 16 Mark Question with Answers, Syllabus & Question Bank

 

 

CS8082 MACHINE LEARNING TECHNIQUES Syllabus

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.

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.

UNIT III 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 – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.

UNIT IV INSTANT BASED LEARNING
K- Nearest Neighbour Learning – Locally weighted Regression – Radial Bases Functions – Case Based Learning.

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

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