One of the major problems a researcher faces is what is learned from data obtained by various methods and different techniques. This tutorial will discuss and compare topics such as: Statistical Advances and Challenges, as well as Feature Extraction in Computational Intelligence methodologies.

Often a simple model describes the data well, simply because the S/N ratio is too small for detection of more complex structures-which for example is the case with medical data involving human subjects. One has a lot of variability both in intra- and inter-sets of data.

Some important Simple Tools that have been used for a long time are: Linear Regression, Discriminant analysis, Principal Component Analysis etc.

In all of these, the size of the data set matters. Huge data sets create memory problems. The question is how do we handle different data types and how do we handle them? What if the data are correlated? What if we have complex data structures?

In this lecture you will learn more about Computational Intelligence, how to get to know your data and how to do Feature Extraction. Some examples of "features" will be given and different feature extraction methods will be discussed.

Evangelia Micheli-Tzanakou is Professor II and Director of the Computational Intelligence Laboratories ( www.cil.rutgers.edu) in the Department of Biomedical Engineering at Rutgers and an adjunct professor of the University of Medicine and Dentistry of New Jersey. Dr Tzanakou was Chair of the BME Department for 10 years and established the Undergraduate curriculum in the same. She is a Founding Fellow of AIMBE, a Fellow of IEEE and a Fellow of the New Jersey Academy of Medicine. She has published two books: "Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence" was published by CRC Press in January 2000 and co-authored a book with S. Deutsch on "Neuroelectric Systems", published by New York University Press, in 1987. She has published over 250 scientific papers in journals, conference proceedings and book chapters. She has also edited several books and conference proceedings.

Dr. Tzanakou has established the first ever experimental Brain to Computer Interface (BCI), using the ALOPEX algorithm, in 1974. This method is now used for target optimization in Parkinson's disease. ALOPEX has also been used in a wide variety of problems: signal processing, image processing, pattern recognition, transportation and many more.

Dr. Tzanakou is Book Series Editor in Biomedical Engineering for Springer Publishing; Associate Editor for the IEEE Transactions on Neural Networks; on the editorial board of the IEEE Transactions on Information Technology; editorial board of the IEEE Transactions On Nano-bio-sciences, and the editorial board of the new journal "Biomedical Engineering on-line".

She has served as Vice President for Conferences of the IEEE Neural Networks Council. She was the 2003 President of the Neural Networks Society; Chair of the IEEE Awards Board in 2003 and 2004. Currently she is IEEE Director, Division X for 2005 and 2006.

Dr. Tzanakou has received several awards including: an Outstanding Advisor Award in 1985 from IEEE, in 1992 the Achievement Award of the Society of Women Engineers, in 1995 she was awarded the NJ Women of Achievement Award for the application of neural networks to engineering in medicine and biology. She is the recipient of the IEEE CIS Meritorious Service Award for 2006.

Her research interests include Neural Networks, Information Processing in the brain, Image and Signal Processing applied to Biomedicine, Mammography, Telemedicine, Hearing Aids and electronic equivalents of neurons. She has graduated over 40 Masters and PhDs and currently supervises a number of graduate and undergraduate students.