Prerequisite: Familiarity with Algorithms, Probability, Linear Algebra, Programming
Introduction to the course, revision of linear algebra and probability (3 hours)
Regression: linear regression, ridge regression (3 hours)
- Classification: (9 hours)
- Linear discriminant analysis, logistic regression, perceptrons,
- support vector machines, Bayes classifier, decision tree.
- Nonparametric methods: k-nearest neighbours, Parzen window.
Principal component analysis, Canonical correlation analysis (3 hours)
Evaluation and Model Selection: ROC Curves, Evaluation Measures, Cross validation, Significance tests (3 hours)
Ensemble methods: boosting, bagging, random forests (3 hours)
- Clustering: (9 hours)
- k-means, hierarchical, density based clustering
- Gaussian mixture model
Sequential Learning : hidden Markov model (6 hours)
- Neural network : feedforward NN (3 hours)
- State definitions, theorems/results, algorithms related to key concepts
- Apply standard techniques to solve known problems
- Given a task, derive a learning model by defining appropriate loss function, regulariser, optimization problem and stating the best possible solution.
- Analyse and compare models and algorithms with respect to their complexity, performance and applicability
- Develop models/algorithms with small modifications of existing standard techniques for a modification of known task
- Richard Duda, Peter Hart, David Stork, Pattern Classification, 2nd Ed, John Wiley & Sons, 2001. ISBN 9788126511167
- Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845.
- Tom Mitchell. Machine Learning. McGraw-Hill. ISBN 0070428077.
- Shai Shalev-Shwartz, and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. ISBN 978-1-107-05713-5.