MTech Machine Learning syllabus for 3 Sem 2020 scheme 20MAU331

Module-1 Introduction 0 hours

Introduction:

Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning.

 

Concept Learning:

Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias.

Module-2 Decision Tree Learning 0 hours

Decision Tree Learning:

Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning

A d v e r t i s e m e n t
Module-3 Artificial Neural Networks 0 hours

Artificial Neural Networks:

Introduction, Neural Network representation, Appropriate problems, Perceptrons, Back propagation algorithm

Module-4 Bayesian Learning 0 hours

Bayesian Learning:

Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm

Module-5 Evaluating Hypothesis 0 hours

Evaluating Hypothesis:

Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing learning algorithms.

 

Instance Based Learning:

Introduction, k-nearest neighbour learning, locally weighted regression, radial basis function, cased-based reasoning,

 

Reinforcement Learning:

Introduction, Learning Task, Q Learning

 

Course outcomes:

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

CO1: Understand the concept of machine learning and able to apply it to the real world

CO2: Recall the problems for machine learning and select the either supervised, unsupervised or reinforcement learning.

CO3: Understand theory of probability and statistics related to machine learning

CO4: Illustrate concept instance based learning and evaluate the hypothesis 

 

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) Tom M. Mitchell, Machine Learning , McGraw Hill Education. India Edition 2013

(2) Oliver Theobald, Machine Learning For Absolute Beginners: A Plain English Introduction Scatter plot Press 2 nd Edition, 2013

 

Reference Books

(1) Trevor Hastie, Robert Tibshirani, Jerome, The Elements of Statistical Learning Springer series in statistics.

(2) Andy Grey , Machine Learning: The Ultimate Guide for Beginners and Starters , Amazon Asia-Pacific Holdings Private Limited, Kindle Edition

(3) Ethem Alpaydın,, Introduction to machine learning,, MIT press. 3rd Edition, 2014

 

Note: In case expertise is not available in the parent department, CSE or ECE department faculty shall handle this course

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