Practice Anomaly Detection: Identifying The Unusual (2.2) - Unsupervised Learning & Dimensionality Reduction (Weeks 10)
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Anomaly Detection: Identifying the Unusual

Practice - Anomaly Detection: Identifying the Unusual

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the main purpose of anomaly detection?

💡 Hint: Think about unusual occurrences in datasets.

Question 2 Easy

Name one key algorithm used in anomaly detection.

💡 Hint: Consider algorithms that specifically focus on outliers.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main focus of anomaly detection?

To identify rare items or events.
To cluster similar data points.
To classify labeled data.

💡 Hint: Think about what makes 'anomaly' different.

Question 2

Isolation Forest explicitly isolates anomalies. True or False?

True
False

💡 Hint: Remember its core principle of isolation.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Imagine you have a dataset of network traffic. How would you approach detecting anomalies using both Isolation Forest and One-Class SVM? Describe the steps and rationale behind your choices.

💡 Hint: Think about how layered detection enhances accuracy.

Challenge 2 Hard

Design a simple anomaly detection system for a medical patient monitoring system. What algorithms would you use and why?

💡 Hint: Consider real-time monitoring aspects and data dimensionality in your design.

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Reference links

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