Practice Module 5: Unsupervised Learning & Dimensionality Reduction (1) - Unsupervised Learning & Dimensionality Reduction (Weeks 10)
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Module 5: Unsupervised Learning & Dimensionality Reduction

Practice - Module 5: Unsupervised Learning & Dimensionality Reduction

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary purpose of Gaussian Mixture Models?

💡 Hint: Think about how GMMs differ in assigning data points to clusters.

Question 2 Easy

What is the key function of PCA?

💡 Hint: Consider what happens to data with many features when using PCA.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a key advantage of GMMs compared to K-Means?

GMMs always use spherical clusters.
GMMs assign probabilities to data points instead of strict cluster memberships.
GMMs require labeled data.

💡 Hint: Think about how uniquely each method categorizes data points.

Question 2

True or False: PCA is used primarily for data visualization rather than noise reduction.

True
False

💡 Hint: Recall the dual objectives of PCA.

3 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a dataset with features that are highly correlated. Describe why feature extraction might be a better approach than feature selection in this scenario.

💡 Hint: Think about the impact of correlation among features on interpretation.

Challenge 2 Hard

Given a high-dimensional dataset with clear, non-spherical clusters, would you select GMM or K-Means? Justify your choice.

💡 Hint: Consider the nature of the cluster shapes in your decision.

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Reference links

Supplementary resources to enhance your learning experience.