Subject Code: ID6L004 |
Name: Machine Learning and Data Analytics-II |
L-T-P: 3-0-0 |
Credit: 3 |
Prerequisite: None |
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Text/Reference Books:
1. |
Bishop, C., Pattern Recognition and Machine Learning, Springer, 2006. |
2. |
Murphy, K., Machine Learning: A Probabilistic Perspective, MIT Press, 2012. |
3. |
Koller D. and Friedman N. : Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 |
4. |
Simon H., Neural Networks and Learning Machines Prentice Hall, Third Edition, 2008. |
5. |
Timothy J. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, 2010. |
6. |
Montgomery, D. C., and G. C. Runger, Applied Statistics and Probability for Engineers. John Wiley & Sons, Sixth Edition, 2013. |
7. |
Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. |
8. |
NPTEL lectures on Introduction to Machine Learning |