The post introduce the principle of probabilistic modelling with focus on how to learn parameters of probabilistic model using maximum likehood, bayesian estimation and the maximum aposterior approximation.
The post introduce the basics principle of probability and information theory and their application to machine learning.
The post presents the basic of machine learning with a focus on supervised learning ( linear regression) problem and how to implement it in python.