- 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
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
1. Tom M. Mitchell, ―Machine Learning‖, McGraw-Hill Education (India) Private Limited, 2013.
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.
- Regulation 2017 GE8151 Problem Solving and Python Programming Syllabus
- Regulation 2017 CS8251 Programming in C Syllabus
- 2017 Regulation CS8391 Data Structures Syllabus
- Regulation 2017 CS8392 Object Oriented Programming Syllabus
- 2017 Regulation Computer Science Engineering Syllabus
- Regulation 2017 HS8151 Communicative English Syllabus
- Regulation 2017 MA8151 Engineering Mathematics I Syllabus
- 2017 Regulation PH8151 Engineering Physics Syllabus
- 2017 Regulation CY8151 Engineering Chemistry Syllabus
- 2017 Regulation GE8152 Engineering Graphics Syllabus
- Regulation 2017 HS8251 Technical English Syllabus
- 2017 Regulation MA8251 Engineering Mathematics II Syllabus
- Regulation 2017 PH8252 Physics for Information Science Syllabus
- BE8255 Basic Electrical and Electronics and Measurement Engineering Syllabus
- Regulation 2017 GE8291 Environmental Science and Engineering