Practice Summary - 13.9 | 13. Privacy-Aware and Robust Machine Learning | Advance Machine Learning
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Practice Questions

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Question 1

Easy

What is differential privacy?

πŸ’‘ Hint: Think about the impact of individual data points.

Question 2

Easy

Name one advantage of federated learning.

πŸ’‘ Hint: Consider privacy implications.

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 differential privacy ensure?

  • A. Higher accuracy
  • B. Data leakage
  • C. No significant change in output when data is added/removed
  • D. More data points

πŸ’‘ Hint: Think about the definition of differential privacy.

Question 2

True or False: Federated learning requires centralizing all data on a server.

  • True
  • False

πŸ’‘ Hint: Consider the core principal of federated learning.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design an experiment that tests the robustness of a machine learning model against adversarial attacks. What would be your metrics for success?

πŸ’‘ Hint: Consider various attack types and their impact on output.

Question 2

Analyze the trade-offs between privacy and utility in a machine learning context. What strategies could be employed to balance both?

πŸ’‘ Hint: Think about methods that can increase privacy without crippling performance.

Challenge and get performance evaluation