🗓️ Week 03 - Fundamentals of Classification
Theme: Supervised Learning
🚧 This website is still in construction. The pages will keep being updated over the next few weeks.
Welcome to Week 3!
This week, we’ll look at classification in more details. Different to regression, where we predict a continuous outcome, classification is used to predict a categorical outcome. For example, we might want to predict whether an email is spam or not (a binary outcome) or the type of animal in a picture (a multi-class outcome).
The main algorithms we will use to cover this foundational topic are logistic regression, which is a type of generalized linear model (GLM) and k-nearest neighbours. We’ll also explore examples of metrics used to assess classification model performance such as the confusion matrix and the receiver operating characteristic (ROC) curve.
The topic of your formative (due on W04) will be released this week.
There will also be some homework to prepare for week 4’s lab. Stay tuned!