Practice Algorithms (9.10.3) - Reinforcement Learning and Bandits - Advance Machine Learning
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Algorithms

Practice - Algorithms

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does a contextual bandit allow an agent to do?

💡 Hint: Think about why context is crucial for decision making.

Question 2 Easy

Name one algorithm discussed for tackling contextual bandit problems.

💡 Hint: Consider which algorithm bases its decisions on linear models.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the fundamental concept behind contextual bandits?

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

💡 Hint: Reflect on how context supports better decisions.

Question 2

LinUCB adapts to new data by using which technique?

True
False

💡 Hint: Consider how a linear model functions.

3 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Focus on what context features would be pivotal for the algorithm.

Challenge 2 Hard

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

💡 Hint: Think about the role of past performance in shaping current actions.

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