## Readme for HW#3 [CMPUT 466 / 551 ]

• For Questions 1-2, 4, 5(b,c,d,e), 6, 7{The comparison part,b}, 8-9: submit in paper form to the course assignment drop box (on the first floor of CSC). Please ensure that your name, login, and student ID are written on your assignment.

• For Questions 3, 5(a), 7(a,c,d): First zip your codes and figures into a single file, then email that file to c466-submit@cs.ualberta.ca.
(Be sure to watch the newsgroup for possible further directions.)
You will need to use the code in ZIP file.

#### Question 3

Datasets:

• The training data is randomly generated by the test_gp functions, so every time you make a run (a plot) you get different results.

You should hand in:

• The Matlab code (well commented!),
• GP_regression.m
• The classification results. You can choose the experiment parameters on your own. Do it to get interesting plots for the conclusion (Please submit both .fig and .jpg files).
• plots for test_fn_1d_1.m function: fn_1d_1_plot_{1,2}.{fig,jpg}
• plots for test_fn_1d_2.m function: fn_1d_2_plot_{1,2}.{fig,jpg}
• plots for test_fn_2d_1.m function: fn_2d_1_plot_{1,2}.{fig,jpg}
• plots for test_fn_2d_2.m function: fn_2d_2_plot_{1,2}.{fig,jpg}
• the running times of the algorithms in seconds.
• a summary about the experiments with your conclusions in PDF format

#### Question 5

Dataset:

• The data set is the one used in Figure 2.1. of the textbook. It has 200 data points. The input is 200x2 dimensional (tr_p) and the output is 200x1 (label) which is either +1 or -1.

You should hand in:

• The Matlab file (well commented!)
• back_prop.m
• The back_prop function will have two inputs P and T, in which P is the network inputs (nx2) and T (nx1) is the network outputs. Note that n is number of training points. It will also have two outputs, IW and LW. IW is a 2x2 matrix for the wights that connect the input layer to hidden layer and it should be in the form of [A,C;B,D]. LW is for the weights that connect hidden layer to output layer. It is 2x1 vector of the form [E;F]. Considering the bias nodes is not necessary here.
• The Results
• Q5a.{fig,jpg}
• This should plot both the data points and the classification boundary. Use 'x' and red color for points with label +1 and 'o' and green color for the points with label -1.
[hint: you do not need to compute the boundary. The first dimension of inputs ranges from [-3,5] and the second dimension ranges from [-2,3]; therefore, you can just consider many points in the input space and plot a red dot for positive points and a green dot for negative points and a black dot for points near zero.]

#### Question 7

You should hand in:

• The Matlab files (well commented!)