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  • 2:26 PM, Saturday, 08 Aug 2020


Course Postgraduate
Semester Electives
Subject Code MA873
Subject Title Graphical and Deep Learning Models

Syllabus

Graphical Models: Basic graph concepts; Bayesian Networks; conditional independence; Markov
Networks; Inference: variable elimination, belief propagation, max-product, junction trees, loopy
belief propogation, expectation propogation, sampling; structure learning; learning with missing
data.
Deep Learning: recurrent networks; probabilistic neural nets; Boltzmann machines; RBMs; sig-
moid belief nets; CNN; autoencoders; deep reinforcement learning; generative adversarial net-
works; structured deep learning; applications.

Text Books
References

1. Koller D. and Friedman, N., Probabilistic Graphical Models: Principles and Techniques, The MIT Press (2009).
2. Barber, D., Bayesian Reasoning and Machine Learning, Cambridge Univ. Press (2012).

3. Bishop, C. M., Pattern Recognition and Machine Learning, Springer (2006).
4. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer (2002).
5. Murphy, K. P., Machine Learning: A Probabilistic Perspective, The MIT Press (2012).
6. Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, The MIT Press (2016).