Model Fitting

This lecture focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling. 

This is the first of a series of tutorials on fitting models to data. In this tutorial, we start with simple linear regression, using least squares optimization.

In this tutorial, we will use a different approach to fit linear models that incorporates the random 'noise' in our data.

This tutorial discusses how to gauge how good our estimated model parameters are.

In this tutorial, we will generalize the regression model to incorporate multiple features.

This tutorial teaches users about the bias-variance tradeoff and see it in action using polynomial regression models.

This tutorial covers how to select an appropriate model based on cross-validation methods. 

This lecture summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.