This page contains links to homework assignments and to data sets used in classroom examples.
|Homework 1 Assignment: From textbook, 2.4,2.8. 2.28, 2.31. The last two problems are computer problems. You can use any language that you want. I suggest either R or Matlab. It is due on Jan 30 in class.|
|Homework 2. It will be due on Monday Feb 13.|
Data is a lot like humans: It is born. Matures. Gets married to other data, divorced. Gets old. One thing that it doesn't do is die. It has to be killed. - Arthur Miller (In speech on Annual Seminar of the American Society for Industrial Security, Boston, September 1988)
|In Fisher's iris data
set, a sample of 150 irises were studied. The measurements are of
type, petal width (PW), petal length (PL),
sepal width (SW), and sepal length (SL) for a sample of 150
irises. The lengths are measured in millimeters. Type 0 is Setosa;
type 1 is Verginica; and type 2 is Versicolor. Source
Fisher, R. A. (1936). "The Use of Multiple Measurements in Axonomic Problems."
Annals of Eugenics 7, 179-188. Files obtained
|The cicada data gives the body weight (in grams), wing length, wing width,
body length (in millimeters), gender, and species for a sample of 13-year
cicadas (Magicicada) collected in the middle Tennessee area.104
cicadas were captured. Source: Ginger Rowell and Robert Grammer, Belmont
College. Files obtained from: http://www.math.uah.edu/statold/sample/sample1.html.
Learning Data Repository: This is a repository of databases, domain
theories and data generators that are used by the machine learning community
for the empirical analysis of machine learning algorithms.|
|GGobi - Statistical visualization package for exploring data.|
|Matlab - The statistical, neural network, and fuzzy logic toolbox will be very useful for processing data for homework and projects.|
to support the Duda, Hart and Stork textbook. This toolbox started as
a course assignment in Dr. Ron Meirís graduate course, Pattern Recognition at
Technion Ė Israel Institute of Technology. The foundation for the toolbox, as
well as most of the basic algorithms, were coded by Elad Yom-Tov and Hilit
Serby. A year later, Igor Makienko and Victor Yosef coded the Voted Perceptron
More about the toolbox here.
Page last edited 02/06/2006