Note : This course is currently under approval. The content displayed below may not be final.
Note : This course not available for PG students.
Objectives and Learning Outcomes
This course will serve as a comprehensive introduction to various topics in machine learning. At the end of the course the students should be able to design and implement machine learning solutions to classification, regression, and clustering problems; and be able to evaluate and interpret the results of the algorithms.
Course Contents
- Introduction to the course, recap of linear algebra and probability theory basics.
- Linear Regression, Ridge Regression, Sensitivity Analysis, Multivariate Regeression.
- Bayesian Classification: Naive Bayes, Parameter Estimation (ML, MAP), Sequential Pattern Classification.
- Evaluation and Model Selection: ROC Curves, Evaluation Measures, Significance tests.
- Non-parametric Methods: k-Nearest Neighbours, Parzen Window.
- Discriminative Learning models: Logistic Regression, Perceptrons, Artificial Neural Networks, Support Vector Machines.
- Dimensionality Reduction: Principal Component Analysis, Fischer's Discriminant Analysis.
- Decision Trees: Splitting Criteria, CART.
- Ensemble Methods: Boosting, Bagging, Random Forests.
- Clustering: Partitional, Hierarchical, density based clustering.
Text Books:
- Pattern Recognition and Machine Learning, Christopher Bishop, Springer 2006.
Reference Books:
- Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 2013.
- Pattern Classification, 2nd Ed., Richard Duda, Peter Hart, David Stork, John Wiley & Sons, 2001.
