Monday March 22, 2004

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Monday March 22, 2004
DSES 6180-01 DATA MINING AND KNOWLEDGE DISCOVERY
Instructor:
Prof. Mark J. Embrechts (x 4009 or 371-4562)
Office hrs:
CII 5217 Thursday 10:00-11:00
Class Time:
Monday/Thursday 4-5:20 pm (Jonson-Rowland Science Center 2C30)
Book: Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall
2003.
LECTURES #22&23: METANEURAL HANDS-ON
About 80 percent of artificial neural network applications are based on the backpropagation
algorithm. This lecture will review backpropagation (history and algorithm) and illustrates the
use of the MetaNeural software to solve some practical neural network problems. We will
discuss a fully connected feedforward (perceptron) type of neural network trained with the
backpropagation algorithm. Neural network applications addressed in the literature range from
pattern recognition, classification, clustering, visualization, (process) control and neuro-control,
game playing, diagnostics (e.g., car industry, medical, manufacturing), forecasting, prediction,
regression models, optimization (e.g. finance), hardware and embedded control implementation,
code simulation, and in-silico drug design. Practical implementations of neural networks will
discuss the bias, the number of hidden layers, the number of neurons, transfer functions,
preprocessing, training parameters, momentum, data preparation and preprocessing, other cost
functions, training strategies, pruning and growing of networks, when to stop training, the curse
of dimensionality, bootstrapping, …
Handouts:
1. Drew Van Camp, “Neurons for Computers,” Scientific American, pp. 170-172, September
1992.
2. Geoffry E. Hinton, “How Neural Networks Learn from Experience,” Scientific American, pp.
145 - 151 (September 1992).
Homework #4 (due April 19)
Experiment with the iris data and Analyze and show your predictions for iris.tes. The homework
should be in a report style and rich on graphics. The purpose of the homework is to show that you
are competent applying a neural network for making predictions on a dataset. You can
additionally run other datasets or use different neural network software.
1
Deadlines:
January 22
January 29
February 16
March 1
March 4
March 18
March 8&11
March 15
April 8
April 19
April 22/26
HW#0 (Web browsing).
Project Proposal
HW #1
Quiz #1 on PLS paper by Svante Wold et al.
HW #2
HW #3
Spring Break
Progress Report
Guest lecture
HW#4
Final Presentations
2
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