1) Reinforcement learning optimizes: A) Expected cumulative reward 2) A model can be accurate and unfair: A) True 3) Fairness constraints: A) May reduce accuracy 4) Ethical AI primarily concerns: A) Fairness, accountability, transparency 5) SGD provides implicit regularization: A)…
1) Random search outperforms grid search when: A) Many irrelevant hyperparameters 2) Does cross-validation reduce variance of performance estimate? A) Yes 3) Is PCA supervised? A) It can be adapted 4) Scaling affects K-Means because: A) It uses distance metric…
1) Universal Approximation Theorem implies: A) Any function can be approximated with enough neurons 2) Softmax ensures: A) Outputs sum to 1 3) Are deep networks always overparameterized? A) It depends on definition 4) Increasing depth without regularization increases: A)…
1) Gradient descent finds global minimum when: A) Function convex 2) Dropout primarily reduces: A) Variance 3) XOR cannot be solved by single perceptron because: A) Not linearly separable 4) Does increasing model complexity always reduce training error? A) Yes…
