Pre-requisites : NIL

Syllabus :
Introduction: Supervised learning, unsupervised le arning and related tools; Data Processing: Identification of features for varied application domains with diverse data (structured or unstructured). Data Visualization: multidimensional scaling; matrix factorization techniques, statistical techniques; Application learning techniques to various data types such as vector data, text data, web data, relational data, sequence data and graphs.

Texts :
1.R. O. Duda, P. E. Hart and D. G. Strok, Pattern Classification, John Wiley, 2007.
2.S. Chakrabarti, Mining The Web: Discovering Knowledge From Hypertext Data, Morgan Kaufmann, 2010.
3. L. Eldéns, Matrix Methods in Data Mining and Pattern Recognition, Society for Industrial and Applied Mathematics, 2007.
4. I. H. Witten, E. Frank and M. A. Hall, Data Mining Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011.

References :