Sorry, you need to enable JavaScript to visit this website.

  • 2:21 PM, Saturday, 08 Aug 2020


Course Postgraduate
Semester Sem. I
Subject Code MA618
Subject Title Foundations of Machine Learning

Syllabus

Machine learning basics: capacity, overfitting and underfitting, hyperparameters and validation
sets, bias & variance; PAC model; Rademacher complexity; growth function; VC-dimension;
fundamental concepts of artificial neural networks; single layer perceptron classifier; multi-layer
feed forward networks; single layer feed-back networks; associative memories; introductory
concepts of reinforcement learning, Markhov decision process.

Text Books
References

1. Mohri, M., Rostamizadedh, A., and Talwalkar, A., Foundations of Machine Learning, The MIT Press (2012).
2. Jordon, M. I. and Mitchell, T. M., Machine Learning: Trends, perspectives, and prospects, Vol. 349, Issue 6245, pp. 255-260, Science 2015.
3. Shawe-Taylor, J. and Cristianini, N., Kernel Methods for Pattern Analysis, Cambridge Univ. Press (2004).
4. Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall (1998).
5. Hassoun, M. H., Fundamentals of Artificial Neural Networks, PHI Learning (2010).
6. Ripley, B. D., Pattern Recognition and Neural Networks, Cambridge Univ. Press (2008).
7. Sutton R. S. and Barto, A. G., Reinforcement Learning: An Introduction, The MIT Press (2017).