Introduction into Python
Useful python packages and tools
Introduction of the Bayesian framework
Maximum likelihood as an approximation of the full Bayesian analysis
Measuring errors from the Maximum Likelihood fits
Practical implementation of the ML fitting in Python
Different ML optimizers in Python
Fitting data with outliers
Markov Chain Monte-Carlo methods
Python packages for performing MCMC analysis
Model selection (AIC, cross-validation, Bayes factors)
Classification using mixture models
Simple machine learning classification algorithms in Python.
Hierchical Bayesian models
Building statistical models using STAN package.