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Practice Questions
Test your understanding with targeted questions related to the topic.
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.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
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.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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.
Question 2
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).
Challenge and get performance evaluation