· 6 min read

Unsupervised Learning: K-means Clustering and Anomaly Detection

Explore core concepts of unsupervised learning, including K-means clustering, optimization strategies, and how anomaly detection systems are designed and evaluated.

#Machine Learning#Unsupervised Learning#Clustering
Unsupervised Learning: K-means Clustering and Anomaly Detection

1. Unsupervised learning

Clustering

What is clustering?

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K-means intuition

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K-means algorithm

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Optimization objective

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Initializing K-means

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Choosing the number of clusters

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Anomaly detection

Finding unusual events

Gaussian (normal) distribution

Anomaly detection algorithm

Developing and evaluating an anomaly detection system

Anomaly detection vs. supervised learning

Choosing what features to use