Discovery: Adaptive Learning Systems, Evolution, Data Mining
We are interested in a variety of forms of machine learning including, to
mention a few topics, supervised and unsupervised algorithms, learning from
humans (knowledge acquisition) as well as from data and environments
(classifiers and pattern recognizers). Our interests cut across the basic
research needed to develop better learning algorithms all the way to
improving usage and outcomes from applying available toolsets to
significant real world problems. A few example research project
descriptions include:
Faculty
Nabil H. Farhat, PhD, New Types of Neural Network Algorithms
John Holmes, PhD, Medical informatics in epidemiology, knowledge discovery and datamining
Steven Kimbrough, PhD, Decision support systems; electronic commerce;
artificial intelligence and computational rationality; logic modeling; evolutionary computation
(including genetic algorithms and genetic programming)
Tony Smith, PhD, Probabilistic models in spatial interaction behavior
Santosh S. Venkatesh, PhD, Neural Networks,
Computational Learning Theory and Information Theory,
Pattern Recognition
Iraj Zandi, PhD, Neural Network Studies
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