Week 10 (linear regression)

Question 1

basic goal behind linear regression (basic 2 var example)

Answer 1

Question 2

in linear regression what are the variables

Answer 2

Question 3

in lin reg how do we determine which model best fits the data

Answer 3

the model with the min sum of errors on training data

Question 4

this is the linear regression equation, what does it expand to

Answer 4

Question 5

in lin reg, what happens if you specify a too big or too small learning rate

Answer 5

to small: gradient descent can be slow

too big: gradient descent can overshoot the min and fail to converge or even diverge