Practice M-step (5.5.3) - Latent Variable & Mixture Models - Advance Machine Learning
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M-step

Practice - M-step

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does M in M-step stand for?

💡 Hint: Think about what we do in this step.

Question 2 Easy

What is one key purpose of the M-step?

💡 Hint: What does this step improve in the EM algorithm?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the M-step aim to achieve?

Maximize the expected log-likelihood
Estimate missing data
Select the number of components

💡 Hint: Think about the primary action in this step.

Question 2

The M-step can lead to local maxima. True or False?

True
False

💡 Hint: Consider what local maxima means in the context of optimization.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Create a detailed explanation of how the EM algorithm uses the M-step for clustering in GMMs.

💡 Hint: Consider how the estimated responsibilities guide parameter updates.

Challenge 2 Hard

Discuss the significance of running EM from multiple starting points and its effect on the M-step outcomes.

💡 Hint: Link back to the limitations discussed about local maxima in the M-step.

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Reference links

Supplementary resources to enhance your learning experience.