CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation

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CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation

MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation

CS8082                            MACHINE LEARNING TECHNIQUES                          L T P C                                                                                                                             3 0 0 3

OBJECTIVES:

  • To understand the need for machine learning for various problem solving
  • To study the various supervised, semi-supervised and unsupervised learning algorithms in machine learning
  • To understand the latest trends in machine learning
  • To design appropriate machine learning algorithms for problem solving

UNIT I INTRODUCTION                                                   9

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                                                                  9

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                                                                       9

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                            9

K- Nearest Neighbour Learning – Locally weighted Regression – Radial Basis Functions – Case Based Learning.

UNIT V ADVANCED LEARNING                                      9

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

                                                                                                      TOTAL :45 PERIODS

OUTCOMES:

At the end of the course, the students will be able to

  • Differentiate between supervised, unsupervised, semi-supervised machine learning approaches
  • Discuss the decision tree algorithm and indentity and overcome the problem of overfitting
  • Discuss and apply the back propagation algorithm and genetic algorithms to various problems
  • Apply the Bayesian concepts to machine learning
  • Analyse and suggest appropriate machine learning approaches for various types of problems

TEXT BOOK:

  1. Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.

REFERENCES:

  1. Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and
    Machine Learning), The MIT Press 2004.
  2. Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.

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