MACHINE LEARNING Syllabus (15CS73)
Subject Code: 15CS73 Semester: 7 Scheme: CBCS
Text Books:
- Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd edition, springer series in statistics.
- 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
- 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
