Model Selection

Regression is one of the most widely used statistical techniques across the sciences, social sciences, and industry. This course develops both its theory and its practice: how regression models are built and justified, and how to analyse data when they apply. We begin with simple linear regression, then extend to multiple regression in matrix form, and close with what to do when the standard assumptions fail: diagnostics, model selection and validation, and remedial measures. All computation is in R.

The language of probability allows us to coherently and automatically account for uncertainty. This course will teach you how to build, fit, and do inference in probabilistic models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. It will offer a broad view of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses.