Course

Unit I

Introduction: Well-Posed Learning Problems, designing a Learning System, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version spaces and the Candidate-Elimination algorithm, Inductive Bias. Introduction to types of learning: Supervised learning, Semi-Supervised Learning, Unsupervised learning and Reinforcement Learning.

Unit II

Classification and Regression: Learning a class from training, linear, Nonlinear, Multi Class and Multi Label classification. Regression: Simple Linear Regression, Multiple linear regression, Logistic Regression. Decision Tree learning: Introduction, Decision tree representation, Appropriate problems for decision tree learning, The basic decision tree learning algorithm, Issues in decision tree learning, classification and regression Trees (CART).

Unit III

Bayesian Learning: Bayes Theorem and Concept Learning, Maximum Likelihood, Minimum description length principle, Bayes Optimal Classifier, Gibbs Algorithm, Naïve Bayes classifier. Instance-Based Learning: K-Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Functions, Support Vector Machines: The Margin and Support Vectors, linear and Nonlinear Kernels.

Unit IV

Ensemble Learning: Ensemble Learning Model Combination Schemes, Maximum Voting, Averaging, Weighted Averaging, Bagging: Random Forest Trees, Boosting: Gradient Boosting, Adaptive Boosting, Extreme Gradient Boosting, XG Boosting and Adaboost, Stacking.

Unit V

Unsupervised Learning: Clustering: K-means/Kernel K-means. Dimensionality Reduction: PCA and kernel PCA. Matrix Factorization and Matrix Completion. Expectation Maximization, Gaussian Mixture Models, Case Study.

Course Outcomes:

CO1: To be able to formulate machine learning problems corresponding to different

applications.

CO2: Exploring and implementing supervised learning through regression and decision tree

learning.

CO3: Understand the instance-based learning and classification methods.

CO4: Understand the Ensemble learning methods.

CO5: Exploring and implementing unsupervised learning algorithms.

CO-PO Mapping:

Text Books/ Reference Books:

1. Tom M. Mitchell, Machine Learning, McGraw Hill, 1997.
2. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Prentice Hall of India, 3^rd Edition, 2014.
3. Stephen Marsland, Machine Learning: An Algorithmic Perspective, Taylor & Francis (CRC), 2014.
4. Haroon D, Python Machine Learning Case Studies, Apress, 2017.
5. Harrington, P, Machine learning in action, Manning Publications Co., 2012.
6. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern classification, John Wiley & Sons, 2001.
7. Peter Flach, Machine Learning, Cambridge University Press, 2012.