$1.$ Based on the data set in Table$1$ and Table $3$ in Appendix, answer to the following questions.

a$.$ Estimate the rent for a new property in the market which is a $3500$ sqft house.

b$.$ Show whether your estimated model is a good fit on the dataset or not.

c$.$ Explain "overfitting" and "underfitting" using "bias" and "variance" definitions. What remedies do you suggest for each situation?

Table$1.$ Rent data

$2.$ In question$1,$ assume that we have another column of data which is the number of bedrooms.

a$.$ What is your proposed solution for finding similar houses based on the two features: sqft and number of bedrooms.

b$.$ Why do you suggest this method $($compare it with other machine learning methods you learned in our class$)$

c$.$ Explain the process using the equations you learned in the course.

d$.$ Solve it numerically for one iteration and explain the convergence criteria $($when the algorithm stops$)$

Table$2$ shows the probabilities of effectiveness of physical therapy.

Assume that a new patient who suffers from a shoulder movement problem, but she does not have a neck pain. Explain if the patient needs physical therapy or not. $($find $"$a$"$ and $"\backslash ($ b $\backslash )"$ values in the table $3$ in appendix$)$ Table $2.$ effectiveness of physical therapy

$4.$ Give an example of traveling sales man problem. Use your desired costs on the search graph that you will draw.

a$.$ Solve the problem using simulated annealing for two iterations.

b$.$ Solve the problem using either DFS or BFS search algorithm.

c$.$ Solve the problem using $\backslash ($ A$^\{*\}\backslash )$ search algorithm