Friday 27 July 2018

MACHINE LEARNING Syllabus (15CS73)

MACHINE LEARNING Syllabus (15CS73)



Subject Code: 15CS73                     Semester: 7              Scheme: CBCS

Text Books:
  1. Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education
Reference Books:
  1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd edition, springer series in statistics.
  2. Ethem Alpaydın, Introduction to machine learning, second edition, MIT press.

Module – 1: Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7
  • 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: Text Book1, Sections: 3.1-3.7
  • 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
Module – 3: Text book 1, Sections: 4.1 – 4.6
  • Artificial Neural Networks: 
    • Introduction
    • Neural Network representation
    • Appropriate problems
    • Perceptrons
    • Backpropagation algorithm
Module – 4: Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12
  • 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: Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3
  • Evaluating Hypothesis: 
    • Motivation
    • Estimating hypothesis accuracy
    • Basics of sampling theorem
    • General approach for deriving confidence intervals
    • Difference in error of two hypothesis
    • Comparing learning algorithms
  • Instance Based Learning: 
    • Introduction
    • k-nearest neighbor learning
    • locally weighted regression 
    • radial basis function cased-based reasoning
  • Reinforcement Learning: 
    • Introduction
    • Learning Task 
    • Q Learning

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