MTech Machine Learning Techniques syllabus for 3 Sem 2020 scheme 20SCE321

Module-1 INTRODUCTION, CONCEPT LEARNING AND DECISION TREES 0 hours

INTRODUCTION, CONCEPT LEARNING AND DECISION TREES

Learning Problems – Designing Learning systems, Perspectives and Issues – Concept Learning – Version Spaces and Candidate Elimination Algorithm – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search

Module-2 NEURAL NETWORKS AND GENETIC ALGORITHMS 0 hours

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 Evolution and Learning.

A d v e r t i s e m e n t
Module-3 BAYESIAN AND COMPUTATIONAL LEARNINGL 0 hours

BAYESIAN AND COMPUTATIONAL LEARNINGL

Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier– Bayesian Belief Network – EM Algorithm – Probably Learning – Sample Complexity for Finite and Infinite Hypothesis Spaces – Mistake Bound Model.

Module-4 INSTANT BASED LEARNING AND LEARNING SET OF RULES 0 hours

INSTANT BASED LEARNING AND LEARNING SET OF RULES:

K- Nearest Neighbor Learning – Locally Weighted Regression – Radial Basis Functions –Case-Based Reasoning – Sequential Covering Algorithms – Learning Rule Sets – Learning First Order Rules – Learning Sets of First Order Rules – Induction as Inverted Deduction – Inverting Resolution

Module-5 ANALYTICAL LEARNING AND REINFORCED LEARNING 0 hours

ANALYTICAL LEARNING AND REINFORCED LEARNING:

Perfect Domain Theories – Explanation Based Learning – Inductive-Analytical Approaches - FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning

 

Course outcomes:

At the end of the course the student will be able to:

  • Choose the learning techniques with this basic knowledge.
  • Apply effectively neural networks and genetic algorithms for appropriate applications.
  • Apply Bayesian techniques and derive effectively learning rules.
  • Choose and differentiate reinforcement and analytical learning techniques

 

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

  • The question paper will have ten full questions carrying equal marks.
  • Each full question is for 20 marks.
  • There will be two full questions (with a maximum of four sub questions) from each module.
  • Each full question will have sub question covering all the topics under a module.
  • The students will have to answer five full questions, selecting one full question from each module.

 

Textbook/ Textbooks

1 Machine Learning Tom M. Mitchell McGraw-Hill Education 2013

 

Reference Books

1 Introduction to Machine Learning EthemAlpaydin PHI Learning Pvt. Ltd 2 nd Ed., 2013

2 The Elements of Statistical Learning T. Hastie, R. Tibshirani, J. H. Friedman Springer 1st edition, 2001

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