Consider the problem of predicting the weather temperature. We apply a polynomial regression, and our hypothesis...

 

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Consider the problem of predicting the weather temperature. We apply a polynomial regression, and our hypothesis is defined as follows: The cost function is given by: 2. h(x)e = 3. where m represents the number of the samples, and y() is the actual output for sample i a. Find the following partial derivatives (show/explain your work): aj 1. 20 aj 201 aj 20 40xx00x +00 - J(00, 0,02)=1( y(i) ho (x(i))) 2m b. Write the formula to update 0 that minimizes the cost function? Consider the problem of predicting the weather temperature. We apply a polynomial regression, and our hypothesis is defined as follows: The cost function is given by: 2. h(x)e = 3. where m represents the number of the samples, and y() is the actual output for sample i a. Find the following partial derivatives (show/explain your work): aj 1. 20 aj 201 aj 20 40xx00x +00 - J(00, 0,02)=1( y(i) ho (x(i))) 2m b. Write the formula to update 0 that minimizes the cost function?