You may include equations and/or graphs

1. Explain bias and variance. Explain the bias/variance trade off. 2. What do you mean by the cost function? 3. For predicting continuous values, we use linear regression but for predicting classes we use logistic regression. Why do we use the sigmoid function for logistic regression? 4. What is the difference between Maximum likelihood and negative log likelihood? Why do we take negative log of the cost function in logistic regression? 5. Gradient descent is an optimization algorithm through which the hyperparameters of your hypothesis are updated, based on the cost function. Can you explain how parameters are updated with gradient descent? 6. What are the benefits of using k-fold cross validation?

Please Cite your sources