Practice Introduction and Motivation - 9.10.1 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.10.1 - Introduction and Motivation

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a Contextual Bandit?

πŸ’‘ Hint: Think of how user data may influence recommendations.

Question 2

Easy

How does LinUCB differ from traditional MAB?

πŸ’‘ Hint: Focus on the role of context in LinUCB.

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 Contextual Bandits use to inform their choices?

  • Only past rewards
  • Contextual information
  • Random choice

πŸ’‘ Hint: Think about personalization in recommendation systems.

Question 2

True or False: Contextual Bandits do not consider context.

  • True
  • False

πŸ’‘ Hint: Remember what sets Contextual Bandits apart from MAB.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a Contextual Bandit algorithm for a new streaming service that considers user viewing history and preferences.

πŸ’‘ Hint: Consider what features best reflect user interests.

Question 2

Discuss the pitfalls of implementing Contextual Bandits in real-time systems.

πŸ’‘ Hint: Reflect on the dynamic nature of user preferences.

Challenge and get performance evaluation