Foundations of Statistical Science

Description

This course aims to provide the theoretical background and implementation methods of various statistical models central to modern data science. Considering students with limited statistical knowledge, the first half covers basics of probability and stochastic processes, introduces various statistical models as general forms of linear regression models, and explains them with data examples. It also discusses recent developments in regression models within machine learning. The latter half covers basic Bayesian model theory and inference, along with their applications in machine learning.