### CS8082 - MACHINE LEARNING TECHNIQUES (Syllabus) 2017-regulation Anna University

## CS8082 - MACHINE LEARNING TECHNIQUES (Syllabus) 2017-regulation Anna University

CS8082 |
MACHINE LEARNING TECHNIQUES |
LPTC |
---|

**3003**

**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 learn the new approaches in machine learning

• To design appropriate machine learning algorithms for problem solving

• To study the various supervised, semi-supervised and unsupervised learning algorithms in machine learning

• To learn the new approaches 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 Bases 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

• Apply specific supervised or unsupervised machine learning algorithm for a particular problem

• Analyse and suggest the appropriate machine learning approach for the various types of problem

• Design and make modifications to existing machine learning algorithms to suit an individual application

• Provide useful case studies on the advanced machine learning algorithms

• Apply specific supervised or unsupervised machine learning algorithm for a particular problem

• Analyse and suggest the appropriate machine learning approach for the various types of problem

• Design and make modifications to existing machine learning algorithms to suit an individual application

• Provide useful case studies on the advanced machine learning algorithms

**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.

2. Stephen Marsland, ―Machine Learning: An Algorithmic Perspective‖, CRC Press, 2009.

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