Prerequisite : Probability and Statistics, Machine Learning
Taught by : Adway Mitra
Teaching Assistents :
Sumanta Chandra Mishra Sharma
Ashraf Haroon Rashid
Probability: definitions, common discrete and continuous distributions, priors and posteriors
Estimation: Maximum Likelihood and Bayesian, Bias of estimation, interval estimation
Law of Large Numbers, Central Limit Theorem
Sampling: rejection, adaptive, importance, Metropolis-Hastings, Markov Chain Monte Carlo
Gaussian Mixture Models, Hidden Markov Models, E-M algorithm
Linear models for regression and classification, generalized linear models, Bayesian extension
Testing of hypothesis, model validation
Non-parametric Inference
Latent Dirichlet Allocation, Dirichlet and Hierarchical Dirichlet Process, Poisson Process