The following exercises provide a thorough introduction to machine learning with a specific focus on linear models. The first exercise walks through the basics of projections and explains how they can be used to fit linear models to data. The second exercise asks you to implement your own linear model solver. Both exercises ask you to reflect on the methods and explain your thoughts. The linear model is extremely important to understand since the rest of the course's machine learning theory builds on it. We therefore highly recommend you use every opportunity you can to ask for help if there is something you have trouble understanding.