Introduction to Bayesian Learning

Topics that will be covered include: the Bayesian learning paradigm: prior and posterior distributions, Bayesian point estimation, credible intervals, hypothesis testing, linear and logistic regression. Bayesian Approaches in Machine Learning with specific emphasis on Bayesian optimization, Bayesian Neural Network and Bayesian AB testing.

On the more computational aspects we will cover: Monte Carlo integration, Markov chains, Markov chain Monte Carlo (MCMC) methods, Adaptive MCMC, MCMC convergence diagnostics, Approximate Bayesian Computation.

Prerequisites. Introduction to statistical inference: notion of population, sample, point estimator, confidence interval, hypothesis testing, linear regression. Having taken the course “Introduction to Data Science” or a similar course covering the basics of Statistical inference is very beneficial.

In presence