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AI From Scratch/Phase 02/Lesson 16/~75 minutes

Anomaly Detection

BuildNo prerequisites

Normal is easy to define. Abnormal is whatever doesn't fit.

Back to phaseML Fundamentals Next lessonHandling Imbalanced Data

Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Phase 02

ML Fundamentals

01What Is Machine Learning 02Linear Regression 03Logistic Regression 04Decision Trees and Random Forests 05Support Vector Machines 06K-Nearest Neighbors and Distances 07Unsupervised Learning 08Feature Engineering & Selection 09Model Evaluation 10Bias-Variance Tradeoff 11Ensemble Methods 12Hyperparameter Tuning 13ML Pipelines 14Naive Bayes 15Time Series Fundamentals 16Anomaly Detection 17Handling Imbalanced Data 18Feature Selection

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Resources

Liu et al., Isolation Forest (2008)Reading Breunig et al., LOF: Identifying Density-Based Local Outliers (2000)Reading scikit-learn Outlier Detection docsReading Chandola et al., Anomaly Detection: A Survey (2009)Reading Goldstein and Uchida, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms (2016)Reading Anomaly DetectorSkill