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Supervised and Unsupervised Machine Learning

A quick tour of Unsupervised Learning

SupaeroDataScience/machine-learning

Supervised and Unsupervised Machine Learning

SupaeroDataScience/machine-learning

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Introduction
Introduction
A geometrical approach to ML
A geometrical approach to ML
- SVMs, the bias-variance tradeoff and a bit of kernel theory
- An application of SVMs to multi-label classification
A probabilistic approach to ML
A probabilistic approach to ML
Mimicking the human brain, the connectionist approach
Mimicking the human brain, the connectionist approach
- Neural networks
Commitee-based methods
Commitee-based methods
Perspectives
Perspectives
- Explainability in ML

A quick tour of Unsupervised Learning🔗

This class links some important concepts seen in the previous classes of Statistics and the Introduction to Statistical Learning, with the concept of Unsupervised Learning and its specificities.

Notebook (colab)