ποΈ Week 03 - Fundamentals of Classification
Theme: Supervised Learning
The lecture notebook has been updated to remove some typos.
The lecture notebook contains its own solutions (i.e there is no separate solutions file): you can render the lecture directly or open it in visual mode and execute the code snippets one by one!
Welcome to Week 3!
This week, weβll 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).
The topic of your formative (due on W04) will be released this week.
There will also be some homework to prepare for week 4βs lab. Stay tuned!
π©π»βπ« Lecture Material
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Material
This week, we wonβt use slides. Instead, we will use the following Quarto markdown file: