- Supervised learning: decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminant analysis, loss regularization framework for classification, Support vector Machines Regression methods: least-square regression, kernel regression, regression trees Unsupervised learning: k-means, hierarchical, EM, non-negative matrix factorization, rate distortion theory.

- Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
- Pattern recognition and machine learning by Christopher Bishop.
- Selected papers.

Pre-requisite | : | Remedial co-requisite: Mathematical foundations (Separately proposed by Prof. Saketh Nath) Recommended parallel courses: CS709 (Convex optimization) |

Total credits | : | 6 |

Type | : | Theory |

Duration | : | |

Name(s) of other Academic units to whom the course may be relevant | : | N/A |