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Practical Machine Learning
1. Looking Toward the Future
2. The Shape of Anomaly Detection
Finding “Normal”
If you enjoy math, read this description of a probabilistic model of “normal”…
Human Insight Helps
Finding Anomalies
Once again, if you like math, this description of anomalies is for you…
Take-Home Lesson: Key Steps in Anomaly Detection
A Simple Approach: Threshold Models
3. Using t-Digest for Threshold Automation
The Philosophy Behind Setting the Threshold
Using t-Digest for Accurate Calculation of Extreme Quantiles
Issues with Simple Thresholds
4. More Complex, Adaptive Models
Windows and Clusters
Matches with the Windowed Reconstruction: Normal Function
Mismatches with the Windowed Reconstruction: Anomalous Function
A Powerful But Simple Technique
Looking Toward Modeling More Problematic Inputs
5. Anomalies in Sporadic Events
Counts Don’t Work Well
Arrival Times Are the Key
And Now with the Math…
Event Rate in a Worked Example: Website Traffic Prediction
Extreme Seasonality Effects
6. No Phishing Allowed!
The Phishing Attack
The No-Phishing-Allowed Anomaly Detector
How the Model Works
Putting It All Together
7. Anomaly Detection for the Future
A. Additional Resources
GitHub
Apache Mahout Open Source Project
Additional Publications
About the Authors
Colophon
Copyright
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