Practice Module Objectives (for Week 10) (1.1) - Unsupervised Learning & Dimensionality Reduction (Weeks 10)
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Module Objectives (for Week 10)

Practice - Module Objectives (for Week 10)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does GMM stand for?

💡 Hint: It involves clustering based on probability distributions.

Question 2 Easy

What is the purpose of feature selection?

💡 Hint: Think about selecting essential ingredients for a recipe.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the 'soft assignment' feature of GMM allow?

Rigid grouping of data points
Probabilistic assignment to multiple clusters
Handling only spherical clusters

💡 Hint: Think about how data points can belong to more than one group.

Question 2

True or False: Anomaly detection is always a supervised learning task.

True
False

💡 Hint: Consider if the anomalies are known in advance.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a high-dimensional dataset from customer behavior with some anomalies. Outline how you would approach identifying these anomalies using the methods discussed.

💡 Hint: Combine techniques strategically for best results.

Challenge 2 Hard

Given a dataset with both substantial and irrelevant features, propose an approach for managing dimensionality effectively.

💡 Hint: Think about how to maintain clarity while reducing dimensions.

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Reference links

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