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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define the Multi-Armed Bandit problem.
π‘ Hint: Think of a gambler faced with several slot machines.
Question 2
Easy
What is the main goal of using exploration strategies in MAB?
π‘ Hint: Think about maximizing rewards over time.
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 primary goal of the Multi-Armed Bandit problem?
- To explore all options equally
- To maximize cumulative rewards
- To minimize the number of trials
π‘ Hint: Remember the gambling analogy.
Question 2
True or False: Contextual bandits do not use extra information to inform their decisions.
- True
- False
π‘ Hint: Consider what 'context' means.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a scenario where an online platform has to decide which of three ad campaigns to run based on click-through rates. Discuss the implications of using UCB versus Thompson Sampling in this context.
π‘ Hint: Think about how each strategy approaches uncertainty and the nature of collected data.
Question 2
Imagine a recommendation system that uses bandit strategies. Design a simple framework for how you would implement this system with emphasis on balancing exploration versus exploitation.
π‘ Hint: Consider how user interactions can inform better recommendations over time.
Challenge and get performance evaluation