Part (e): For each confusion matrix, compute the following quantities: Accuracy False positive rate False negative rate

The following are multi-part problems.

If a problem is a linearly separable what kind of model should you use? Would you use a deep neural network?

[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.)