Practice E-step (5.5.2) - Latent Variable & Mixture Models - Advance Machine Learning
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E-step

Practice - E-step

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Practice Questions

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Question 1 Easy

What is the primary goal of the E-step in the EM algorithm?

💡 Hint: Think about what needs to be inferred from the observed data.

Question 2 Easy

Define a latent variable.

💡 Hint: Consider examples like intelligence or preferences.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the E-step of the EM algorithm estimate?

Likelihood of observed data
Posterior probabilities of latent variables
Expected values of data

💡 Hint: Focus on what we do in the first step of EM.

Question 2

True or False: The E-step must happen before the M-step in the EM algorithm.

True
False

💡 Hint: Think about the sequence of operations in the EM algorithm.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Suppose you have a mixture model for customer segmentation, and the E-step estimates that a particular data point has a 60% probability of belonging to segment A and 40% to segment B. Discuss how you would weigh this data point during model training.

💡 Hint: Focus on soft assignments and their impact on parameter updates.

Challenge 2 Hard

Imagine you run a study with latent variables representing student engagement levels and only have their grades. Describe how the E-step can be implemented in this scenario.

💡 Hint: Consider the relationship between actions (grades) and inferred states (engagement).

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