Subject Code: ID6L004 
Name: Machine Learning and Data AnalyticsII 
LTP: 300 
Credit: 3 
Prerequisite: None 

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 ShalevShwartz and Shai BenDavid. Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. 
8. 
NPTEL lectures on Introduction to Machine Learning 