Skip to main content

Model Fitting I (Intro Lecture)

Difficulty level

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. 

Topics covered in this lesson
  • Purpose of model fitting and the structure of linear models
  • Linear regression with mean-squared error (MSE)
  • Linear regression with maximum likelihood error (MLE)
  • Model fitting for linear models
  • Multiple linear regression and polynomial regression
  • Confidence intervals and bootstrapping
  • Bias-variance trade-off
  • Cross-validation
  • How to assess the quality of model fits  
  • How to compare different fitted models

Experience with Python Programming Language

Back to the course