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Q5: (6 points)Give a scenario that hard margin SVM doesn’t work well, and we need soft margin SVMs.

Q3: (3 points) Answer True or False only: In Boosting, the performance of the final model heavily depends on the performance of the individual weak learners.

Q6: (5 points)Why do we need to use a kernel function in SVM? What does a kernel function try to measure?

Q1: (4 points)What is the two main objective of a Support Vector Machine?

Q4: (3 points)List the names of at least two types of SVM kernels.

Q4: (3 points) Answer True or False only: In DQN, the Q value function is instantiated by a neural network.

Q5: (3 points)In AdaBoosting, there are 8 data instances (i.e., samples). Before updating the weights of the eight samples, the weights of the eight samples are[sample1: 1/8, sample2: 1/8, sample3: 1/8, sample4: 1/8, sample5: 1/8, sample6: 1/8, sample7: 1/8, sample8: 1/8]. After updating the weights of the eight samples, the new weights of the eight samples are[sample1: 0.07, sample2: 0.07, sample3: 0.51, sample4: 0.07, sample5: 0.07, sample6: 0.07, sample7: 0.07, sample8: 0.07]. Which data instances (samples) is/are correctly classified by the decision stump of this iteration?

How many sections must be included in a compliant SDS?

What type of monitoring is required for formaldehyde exposure?

What are ‘triggering events’ under the FTC Rule?