Dr. Eick
COSC 6342“Machine Learning” Assignment1 Spring 2009
Due: Tuesday February, 24, 11p (electronic Submission) or 5 days after the topic was
discussed in the lecture, whichever comes later; problem 5 is due on March 10, 11p
(submit report and software!).
Remark: Problem 6 was updated on March 1, 2008.
1. G---Topic1
Compare reinforcement learning and supervised learning! What are the main differences
between these approaches? Limit your answer to 4-6 sentences!
2. G---Topic2.
Derive the solution for w0 and w1 at the bottom of page 30 of the textbook!
3. G---Topic3
Solve the problem on transparency 9 of Topic3!
4. G---Topic4
Formula 1.4 on page 10 of the Bishop uses regularization for determining the best
weights in curve fitting. What is the motivation to use regularization? How is it used in
the particular approach presented? What alternative methods could be used to accomplish
the goals of regularization?
5. G---Topic5
In this task you will develop a binary classifier (2{0,1}), called MTG (“), called MTG (“mixture of
two Gaussians”), that relies on a parametric approach that uses a mixture of two bi-
variate Gaussians to approximate the density of each class. The classifier will be
evaluated and enhanced on an Arsenic dataset (raw dataset source:
http://www.tlc2.uh.edu/dmmlg/Datasets/arsenic.txt ) and on an artificial dataset using
two-fold cross validation that is repeated 3 times.
Fig. 1: Arsenic Water Well Problem (LongitudeLatitude {safe, dangerous}), called MTG (“)
We assume having two classes C and C’ and a density function is associated with each
class that assesses density using a mixture of two Gaussians G1 and G2. In particular:
G1,C ~ N(G1,C, G1,C)
G2,C ~ N(G2,C, G2,C)
G1,C’ ~ N(G1,C’, G1,C’)
G2,C’ ~ N(G2,C’, G2,C’)
1
