| November 27th, 2020 04:37 PM | |
| prince karak | Sathyabama Institute of Science and Technology ME CSE SCSA7006 Machine Learning Syllabus Sathyabama Institute of Science and Technology ME CSE SCSA7006 Machine Learning Syllabus SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF COMPUTING SCSA7006 MACHINE LEARNING L T P Credits Total Marks 3 0 0 3 100 UNIT 1 INTRODUCTION 9 Hrs. Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate Eliminations– Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search. UNIT 2 NEURAL NETWORKS AND GENETIC ALGORITHMS 9 Hrs. Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms– Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. UNIT 3 BAYESIAN AND COMPUTATIONAL LEARNING 9 Hrs. 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 4 INSTANT BASED LEARNING 9 Hrs. K- Nearest Neighbor Learning – Locally weighted Regression – Radial Bases Functions – Case Based Learning. UNIT 5 ADVANCED LEARNING 9 Hrs. 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. Max. 45 Hrs. TEXT / REFERENCE BOOKS 1. Tom M. Mitchell, “Machine Learning”, McGraw-Hill, 1st edition, 1997. 2. Ethem Alpaydin, “Introduction to Machine Learning (Adaptive Computation and Machine Learning)”, The MIT Press 2004. 3. Hastie. T, Tibshirani. R, Friedman. J. H, “The Elements of Statistical Learning”, Springer,1st edition, 2001. END SEMESTER EXAMINATION QUESTION PAPER PATTERN Max. Marks: 100 Exam Duration: 3 Hrs. PART A: 5 Questions of 6 Marks each – No choice 30 Marks PART B: 2 Questions from each unit of internal choice, each carrying 14 Marks 70 Marks |
