Practice Graphical Models & Probabilistic Inference - 4 | 4. Graphical Models & Probabilistic Inference | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What do nodes and edges represent in a graphical model?

πŸ’‘ Hint: Think about what connects the variables in the graph.

Question 2

Easy

What is a Bayesian Network?

πŸ’‘ Hint: Reflect on how the directionality plays a role in the model.

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 do graphical models primarily represent?

  • Linear equations
  • Joint probability distributions
  • Statistical tests

πŸ’‘ Hint: Consider what a graphical representation would entail in terms of probabilities.

Question 2

Bayesian Networks are based on which type of graphs?

  • True
  • False

πŸ’‘ Hint: Think about the directionality of edges in these models.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a simple Bayesian Network for a basic weather forecasting model considering rain, humidity, and temperature. Define the conditional dependencies.

πŸ’‘ Hint: Consider how one weather condition affects others.

Question 2

Explain how you would use a Markov Random Field for image classification.

πŸ’‘ Hint: Think about the influence of adjacent pixels in an image.

Challenge and get performance evaluation