The Institute for Mathematical Behavioral Sciences presents
“Human and Machine Learning"
March 13-15, 2009
The theory and modeling of learning processes has been studied by scientists from various perspectives and for both natural and artificial systems (humans and computers). Machine learning is a major research area in computer science and statistics, while human learning has long been investigated in social sciences such as in psychology and cognitive science. The two learning approaches are related but different in many ways. In this workshop, we bring together leading experts to present overview and research lectures to explore the interplay of human and machine learning methodologies and algorithms in solving challenging problems in science and engineering. We hope to use the insights from the cognitive study of human learning to inspire novel machine learning methods, and on the other hand expand the processing power of machine learning algorithms to advance our understanding of human learning and the cognitive functions of the brain.
For further information, please contact Janet Phelps, email@example.com.
WILLIAM H. BATCHELDER, Cognitive Sciences, UC Irvine
LI DENG, Speech Research Group, Microsoft Research
JEAN-CLAUDE FALMAGNE, Cognitive Sciences, UC Irvine
TOM GRIFFITHS, Department of Psychology, UC Berkeley
TONY JEBARA, Computer Science, Columbia University
MICHAEL JORDAN, EECS, Statistics, UC Berkeley
MICHAEL LITTMAN, Computer Science, Rutgers
DeLIANG WANG, Computer Science and Engineering, Ohio State University