Tutorial 1: Linear regression with least squares optimization
This is Tutorial 1 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).
- Learn how to calculate the mean-squared error (MSE)
- Explore how model parameters (slope) influence the MSE
- Learn how to find the optimal model parameter using least-squares optimization
Experience with Python Programming Language