Philip Protter

Since PhD students are allowed to choose which seminar to attend regardless of which seminar they are registered for, I usually attended the Statistics Seminar on Mondays.

Statistics, mainly focused on distributions and a bit of probability and linear regression.

First half is linear regression, and second half is machine learning basics.

I learned a lot more about how statistics works on the more mathematical side which was incredibly different than my other stats courses. Rather than just memorizing the formulas I learned how they were derived and what they meant.

Linear regression by a variety of methods

Different statistical models to model situations (linear, logistic, quadratic, etc.), how they work, their assumptions/conditions, deep learning and neural networks

Basic statistical concepts and skills at the college level

Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, collinearity and confounding, model selection, geometry of least squares.

Probability, hypothesis testing, linear regression, expectation, etc

This section felt very different in scope from other sections.