Regression
Introduction
In this project, you will be implementing a regression method from scratch using MATLAB, based on the theory covered in Unit 3. You will need to evaluate your algorithm on some of the datasets that will be provided (you may also add your own if desired), and compare the results to the relevant built-in method in either MATLAB or Python.
Undergraduate and ETIE groups can choose one from of the following:
• Linear regression (without ε term) with gradient descent training
• K-nearest neighbors (using only Euclidean distance)
• Decision trees
Graduate groups can choose from one of the more advanced options below:
• Linear regression (including ε term) with gradient descent training
• K-nearest neighbors (with at least three types of distances)
• Random forest
A separate document will be posted later that provides hints how to approach each of these problems.
