Practice Algorithms - 9.10.3 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.10.3 - Algorithms

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does a contextual bandit allow an agent to do?

πŸ’‘ Hint: Think about why context is crucial for decision making.

Question 2

Easy

Name one algorithm discussed for tackling contextual bandit problems.

πŸ’‘ Hint: Consider which algorithm bases its decisions on linear models.

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 is the fundamental concept behind contextual bandits?

  • Decisions based on random rewards
  • Decisions based on contextual information
  • Decisions without feedback

πŸ’‘ Hint: Reflect on how context supports better decisions.

Question 2

LinUCB adapts to new data by using which technique?

  • True
  • False

πŸ’‘ Hint: Consider how a linear model functions.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset of user interactions with ads, how would you implement an adaptive ad-selection mechanism using LinUCB? Outline the steps.

πŸ’‘ Hint: Focus on what context features would be pivotal for the algorithm.

Question 2

You are tasked with designing a campaign with Contextual Thompson Sampling. How would you model the reward distributions for various ads?

πŸ’‘ Hint: Think about the role of past performance in shaping current actions.

Challenge and get performance evaluation