Teaching

CN550 - Neural and Computational Models of Recognition, Memory, and Attention
CN550 develops neural network models of how internal representations of sensory events and cognitive hypotheses are learned and remembered, and of how such representations enable recognition and recall of these events. Various neural and statistical pattern recognition models, and their historical development and applications, are analyzed. Special attention is given to stable self-organization of pattern recognition and recall by Adaptive Resonance Theory (ART) models. Mathematical techniques and definitions to support fluent access to the neural network and pattern recognition literature are developed throughout the course. Experimental data and theoretical analyses from cognitive psychology, neuropsychology, and neurophysiology of normal and abnormal individuals are also discussed. Course work emphasizes skill development, including writing, mathematics, computational analysis, teamwork, and oral communication.
http://cns.bu.edu/cn550/

CN710 - Advanced Topics in Neural Modeling: Comparative Analysis of Learning Systems
CN710 considers the systematic analysis of supervised learning systems from neural networks, statistics, and artificial intelligence. Supervised learning systems include multi-layer perceptrons (MLP), ARTMAP, support vector machines, and K-nearest neighbors (KNN). Working in collaboration, class members analyze many different algorithms and methods for pre- and post-processing data, with common benchmark problems and system evaluation criteria.
http://cns.bu.edu/cn710/Fall2007/
http://cns.bu.edu/cn710/Fall2006/
http://cns.bu.edu/cn710/Spring2006/