🗓️ Week 07 - Introduction to unsupervised learning and dimensionality reduction

Theme: Unsupervised Learning

Important

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Welcome to the seventh week of this course!

This week, we start our exploration of unsupervised learning:

• we discover what differentiates it from supervised learning and in which cases it is used
• we learn about the first family of unsupervised learning techniques: dimensionality reduction. We’ll explain what dimensionality reduction is about and what it is used for before introducing the most common and arguably the most well-known dimensionality reduction algorithm by far: PCA (Principal Component Analysis). We also have a look at other linear dimensionality reduction algorithms (i.e MCA and FAMD) as well as non-linear dimensionality reduction algorithms: UMAP and auto-encoders.

👩🏻‍🏫 Lecture Material

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Download the Jupyter notebook for this lecture :