[Bonus] part (i): Suppose we now wanted to implement (binary) linear SVM as a neural network. What loss function should we use?

Part (d): What procedure/algorithm would you use to train the neural network you described in part (c)?

Part (a): What type of learning task is Bob trying to solve? (Be specific. Don’t say something generic like ‘supervised learning’.)

Give two possible causes for a ML model being unfair.

Part (b): Consider a single neuron/unit with weight vector , bias , and activation function . The input is a vector . (If the math does not display: weights w, bias b, activation function f, input x.) What is the output of the neuron/unit as a function of the parameters and input?

How can attention be used for interpretability?

Part (f): What change(s) would we need to make if we instead wanted to implement Ridge regression as a neural network? (Be specific.)

Mallory selects two different locations on the ROC curve and you help her produce a confusion matrix for each given the test dataset of 100 instances. Below are the two resulting confusion matrices. Tradeoff 1 Tradeoff 2 Actual 1 (+) 0 (-) Predicted 1 (+) 9 2 0 (-) 41 48 Actual 1 (+) 0 (-) Predicted 1 (+) 40 34 0 (-) 10 16

What are deepfakes?

What is the purpose of the positional encoding in a transformer? Would you use positional encoding in an RNN?