please help me solve this problem. major computer science. Class Machine Learning

Exercise 1 (7points) 1a. Explain the idea of gradient descent algorithm, define the cost function and the rule of updating coefficient of linear regression. 15. Explain why the cost function of logistic regression can be estimated by the following formulation: 1 EO) log h, (x")+(1-yl) log(1-hg(x)] ) mi- 2 10. Assuming at a certain iteration of batch-gradient descent, we reach the global maximum value of the -(0) cost function(" is perfectly O). What will happen next and why? - The algorithm stays at the global maximum - The algorithm updates the coefficient toward the minimum value of cost function Is the answer remaining the same if we are using mini-batch gradient descent or stochastic-gradient descent