Practice Key Challenges (10.7.1) - Causality & Domain Adaptation - Advance Machine Learning
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Key Challenges

Practice - Key Challenges

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does identifiability mean in causal analysis?

💡 Hint: Think about how true causes can be distinguished from mere correlations.

Question 2 Easy

Why is labeled data important?

💡 Hint: Consider what a model needs to understand to make predictions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main challenge relating to causal structure in data analysis?

Identifiability
Irreversibility
Correlation

💡 Hint: Recall what’s necessary to understand the ‘why’ behind data.

Question 2

True or False: Domain generalization allows models to work well on unseen data.

True
False

💡 Hint: Think about the adaptability of models.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a hypothetical experiment to test for the identifiability of a causal relationship in a set of data suspected to have confounding variables.

💡 Hint: Recall how RCTs allow for isolating effects.

Challenge 2 Hard

Propose a method to deal with limited labeled data in a target domain, explaining its advantages.

💡 Hint: Think about knowledge transfer between different subjects.

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Reference links

Supplementary resources to enhance your learning experience.