CMSC727: Neural Modeling is a University of Maryland graduate level artificial intelligence and cognitive science course that exposes the fundamental methods of neural modeling. Surveys historical development and recent research results from both the computational and dynamical systems perspective. Logical neurons, perceptrons, linear adaptive networks, attractor neural networks, competitive activation methods, error back-propagation, self-organizing maps, and related topics. Applications in artificial intelligence, cognitive science, and neuroscience.
The subject is taught to University of Maryland graduate students. Details for the class are here.
I’m Kevin Chen, and this is my personal website. I am a rising final-year student in the CS department at the University of Maryland. ¶ My research interests are in machine learning and theoretical computer science. I enjoy reading, filmmaking, tennis and helping out with Bitcamp.
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