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

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary goal of differential privacy?

πŸ’‘ Hint: Think about how the inclusion of a data point would affect the outcome.

Question 2

Easy

Define robustness in the context of machine learning.

πŸ’‘ Hint: Consider how models react to unexpected changes.

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 main purpose of differential privacy?

  • To add complexity to models
  • To ensure individual data points are indistinguishable
  • To increase data accuracy

πŸ’‘ Hint: Consider what privacy guarantees it provides.

Question 2

True or False: Adversarial training can reduce a model's performance on clean data.

  • True
  • False

πŸ’‘ Hint: Think about the implications of training on different data.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a machine learning model for a medical application that needs differential privacy. Discuss implications of using different privacy techniques and their effects on the model's performance.

πŸ’‘ Hint: Consider use-cases where sensitive data must remain confidential.

Question 2

Critique a current ML model using federated learning regarding its privacy implications. Identify strengths and weaknesses.

πŸ’‘ Hint: Focus on how data is managed between clients and servers.

Challenge and get performance evaluation