Practice Thompson Sampling - 9.9.3.3 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.3.3 - Thompson Sampling

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Thompson Sampling?

πŸ’‘ Hint: Think of its relation to probabilities and rewards.

Question 2

Easy

Define regret in the context of decision-making.

πŸ’‘ Hint: What do we lose by not having the best possible choice?

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 Thompson Sampling aim to optimize in Multi-Armed Bandit problems?

  • Exploration
  • Exploitation
  • Cumulative Reward

πŸ’‘ Hint: What is the ultimate goal when selecting an arm?

Question 2

True or False: Thompson Sampling only focuses on exploration.

  • True
  • False

πŸ’‘ Hint: Consider both terms' definitions.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A website has three different layouts, A, B, and C. Design an experiment using Thompson Sampling to identify the best layout for user engagement. Outline how you would gather data and update your probabilities.

πŸ’‘ Hint: Think about how you'd weigh past clicks when making new choices.

Question 2

Consider an online ad campaign using Thompson Sampling. If the current click-through rate for three ads is 20%, 15%, and 10%, explain how you’d implement Thompson Sampling and justify your choice in the algorithm.

πŸ’‘ Hint: Reflect on how would you update your initial beliefs with new data.

Challenge and get performance evaluation