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Supervised and Unsupervised Machine Learning

Introduction to ML

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

Introduction to Machine Learning🔗

This class provides an introduction to the landscape, zoo, and workflow of Machine Learning.