Week 1

Get started with understanding why and how to detect anomalies in data.

  • Define various types of anomalies
  • Discuss the applications of anomaly detection
  • Explain the statistics and mathematics required

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Week 2

Learn how to build upon probability theory and geometry to identify anomalies. 

  • Describe probabilistic models for anomaly detection
  • Apply extreme value analysis and angle-based techniques
  • Use Python to perform anomaly detection on one- and two-dimensional data

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Week 3

See how to use linear models instead of probabilistic and geometric models.

  • Apply linear regression models and principal component analysis
  • Use support vectors machines (SVMs) for anomaly detection

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Week 4

Explore how to use additional methods based on distance to identify abnormal data.

  • Describe proximity-based methods and the local outlier factor (LOF)
  • Apply the k-nearest neighbors (KNN) algorithm and k-means clustering

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Week 5

Learn how to work with difficult problems that involve high-dimensional data.

  • Understand the difficulties with high-dimensional problems
  • Apply the subspace method with feature bagging and the isolation forest algorithm

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Week 6

Find out how to use supervised learning models and how to work with classifications.

  • Implement cost-sensitive learning algorithms
  • Apply adaptive resampling and boosting methods

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Week 7

Explore how to classify temporal and streaming data.

  • Implement statistical process control
  • Apply streaming anomaly detection using autoregressive models

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Week 8

Measure the performance of an anomaly detection system.

  • Evaluate different techniques and types of anomaly detection
  • Perform analysis on a wide variety of data detection

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