Ridge regression: Write some code which perform L2 regularization. That is, solve OLS with a constraint that the L2 norm of the betas is less than some threshold value. min β Xn i=1 e 2 i subject to Xr i=1 β 2 i ≤ T

Using least squares fitting, you are to fit the data sets to the following models and solve for the parameters ai , where i is the index of the parameter. the input/output data for the systems are linked in the bblearn site. for each of the systems use matlab to plot the supplied data vs. the model fit on one plot. include your code in the solutions. (a) linear fit "lineardata. mat" y=a1x^3 + a2x^2 + a3x + a4 (b) plant fit "plantdata. mat g(s) = a1/(s + a2)

Atool that matches persoal skills qualities interests and talets to a career is called a

[a] create a class called “cycle” which has two instance integer variables as properties, “numberofwheels” and “weight.” create a constructor with two parameters, using the same variable names in the parameter list. assign each variable to numberofwheels” and “weight” respectively. write a separate application to test the class and display its properties. note: do not change the names of the instance variables or the variables listed in the constructor’s parameter list. [b] edit your class cycle by adding a default constructor which will assign the default values of 100 to represent the numberofwheels, and 1000 to represent the weight, by invoking a call to the other constructor. modify your application created in [a] to test the class.