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DS52 Fundamentals of Data Science
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In Ridge regression, as the regularization parameter λ approaches infinity, what happens to the coefficients?
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In Q-learning, the target value for updating Q(s,a) is:
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In AdaBoost, after training a weak classifier, what happens to the weights of misclassified samples?
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Policy Gradient methods differ from Value-Based methods in that:
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How does boosting (e.g., AdaBoost, Gradient Boosting) differ from bagging (Random Forest)?
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How does Random Forest reduce variance compared to a single decision tree?
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The kernel trick in SVM:
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Which property is required for backpropagation to work?
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What is the main advantage of using kernels in SVM?
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Which of the following statements about the bias-variance tradeoff in regularization are true?
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