Homework

This page contains links to homework assignments and to data sets used in classroom examples.

Homework

	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 from: http://www.math.uah.edu/statold/sample/sample1.html

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	Excel Format

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.

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	Excel Format

UCI Machine 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.

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Tools

	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.
	Classification Toolbox-written to support the Duda, Hart and Stork textbook. This toolbox started as a course assignment in Dr. Ron Meir�s graduate course, Pattern Recognition at Technion � 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 algorithms. More about the toolbox here.

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In Class Demonstrations

Hierarchical clustering:

	showclus.m: this routine plots the scattermatrix of the data and calls the hierarchical clustering routine, spheres the data if requested. inputs: data set, each row is a data point, class membership vector, sphering flag (=1 sphere, =0 don't sphere)
	hierclus.m: performs hierarchical clustering for different distance or linkage methods
	Usage Example: using the fisher data set file. load fisher; % loads data file, fisher is data file, orchname-class information for fisher data showclus(fisher,orchnames,1)

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Page last edited 02/06/2006