In this set of slides we discuss 2 population hypothesis tests. Introductory.

Applied problems and solutions for an introductory course.

Introducing Normal Distribution and Discussing CLT with different distributions.

Reviewing Probability. Might later be moved to a special 2333 for ANYL BBA

In this lecture we discuss bias variance trade off.

This lecture should follow the discussions after likelihood and entropy. We discuss the foundations of AIC justification and also show similar likelihood based measures of model comparison indices. Some interesting phenomena are illustrated.

In this lecture we learn what likelihood is and how we can calculate it.

This course will make use of two main sources, 1) The Elements of Statistical Learning by Hastie, Tibshirani and Friedman and 2) Probabilistic Machine Learning by Kevin Murphy. We will also dig into articles.

These slides contain the preliminary set of lectures for the class statistical modeling

Inspired by Dr. McElreath's work, this book explains differences between Models, Processes and Hypothesis and goes on to describe the work building up our model that merges Spatial and Hidden Markov Models.