## Description

1. (10 points) Repeat the problem of applying gradient descent to minimize Ein on the training

dataset (cleveland.train) from Homework 3, but this time scale the features by subtracting the mean and dividing by the standard deviation for each of the features in advance of

calling the learning algorithm (you may find the matlab function zscore useful). Experiment with the learning rate η (you may want to start by trying different orders of magnitude), this time using a tolerance (how close to zero you need each element of the gradient to

be in order to terminate) of 10−6

. Report the results in terms of number of iterations until the

algorithm terminates, and also the final Ein. How does this compare to the Ein of glmfit?

You do not need to submit any code for this problem.

2. (60 points) For this problem, you will be doing LFD Problem 4.4 parts (a) through (d) with

some changes / help / instructions / requirements. First, you can find headers for all the

code you need to implement in your SVN repository for the class. There is also a matlab

script called run expts.m which you can use as an example for how to run your code to

return the results we want. Second, read Problem 4.3 carefully. You can (and will need to)

use the recurrence defined there as well as the formula in 4.3(e).

(a) In addition to answering the question about why we need to normalize f, also prove

that the term to normalize by is qPQ

q=0

1

2q+1 (hint: use the formula in 4.3(e)).

1

(b) Answer the question. For your implementation, we suggest you use glmfit with the

additional options ’normal’,’constant’,’off’.

(c) Answer the question (hint: use the formula in 4.3(e)).

(d) Implement the framework and answer the questions, with the modification that you

only need to look at Qf ∈ {5, 10, 15, 20}, N ∈ {40, 80, 120}, σ2 ∈ {0, 0.5, 1.0, 1.5, 2.0}.

Compute both the median and the mean of the overfit measure applied to many (at

least 500) different datasets for each choice of parameters, and report how these measures vary as a function of the complexity of the true hypothesis, the number of training

examples, and the level of stochastic noise (use line graphs). Explain your observations,

and also comment on the differences you observe between the mean and median measures.

3. (15 points) LFD Exercise 4.4

4. (15 points) LFD Problem 4.8

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