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