Course

Unit 1

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. Validation Metrics. Introduction to types of learning: Supervised learning, Semi-Supervised Learning, Unsupervised learning, and Reinforcement Learning.

Unit 2

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, Polynomial, and Ridge and Lasso 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), Random Forest. Validation Metrics for Classification and Regression.

Unit 3

Bayesian Learning: Nave Bayes classifier. Instance-Based Learning: K-Nearest Neighbour Learning, Radial Basis Functions, Support Vector Machines: The Margin and Support Vectors, linear and Nonlinear Kernels.

Unit 4

Cross Validation (CV) and resampling. 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,Adaboost, Stacking.

Unit 5

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

Text Books / References

Text Books

1. Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997.
2. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Prentice Hall of India, 3rd Edition, 2014.
3. Stephen Marsland, Machine Learning: An Algorithmic Perspective, Taylor & Francis (CRC), 2014.
4. Peter Flach, Machine Learning, Cambridge University Press, 2012.

References

1. Haroon D, Python Machine Learning Case Studies, Apress, 2017.
2. Harrington, P, Machine learning in action, Manning Publications Co., 2012.
3. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern classification, John Wiley & Sons, 2001.