EC875 Probabilistic Models in Machine Learning
Course Name:
EC875 Probabilistic Models in Machine Learning
Programme:
M.Tech(SPML)
Category:
Elective (Ele)
Credits (L-T-P):
(3-0-2) 4
Content:
Probabilistic graphical models, belief networks, decision making, Bayesian linear models, linear Gaussian state space models, Expectation Maximization, Markov models, Bayesian networks, Markov random fields, Markov networks, variational inference, latent variable models, Markov chain Monte Carlo, Kalman Filtering, Particle Filters, Dynamic Bayesian Networks.
References:
David Barber, Bayesian Reasoning and Machine Learning, 1st Ed, Cambridge University Press, 2012.
Jerome H, Friedman, Robert T. Tibshirani, Trevor Hastie, TheElements of Statistical Learning; Data Mining, Inference,
and Prediction, Springer, 2nd Ed, 2009.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT, 2012.
ZoubinGhahramani, Probabilistic Modelling, Machine Learning and the Information Revolution, MIT Tutorial 2012.
Department:
Electronics and Communication Engineering(ECE)