Practice Em Overview (5.5.1) - Latent Variable & Mixture Models - Advance Machine Learning
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EM Overview

Practice - EM Overview

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does the E-step in the EM algorithm involve?

💡 Hint: Think about what is happening to the unobserved data.

Question 2 Easy

What is convergence in the context of the EM algorithm?

💡 Hint: Consider what it means for a method to 'settle down' after repeating it.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of the EM algorithm?

Parameter estimation
Data cleaning
Feature selection

💡 Hint: What do you think it primarily tries to achieve with unseen variables?

Question 2

True or False: The M-step focuses on estimating the posterior probabilities.

True
False

💡 Hint: Remember what each step aims to accomplish.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with missing values, outline how you would apply the EM algorithm, detailing the steps involved.

💡 Hint: How would you approach the visibility of hidden data here?

Challenge 2 Hard

Critique a scenario where the EM algorithm fails to find an accurate model. What are the implications of local maxima in parameter estimation?

💡 Hint: What does this say about the starting point’s influence?

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Reference links

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