8.594J Introduction to Neural Networks


Class Info

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, integrators, attractors, and hybrid computation. Backpropagation, Hebbian, and reinforcement learning. Models of perception, motor control, memory, and neural development.

This class has 9.29 as a prerequisite.

8.594J will not be offered this semester. It will be available in the Spring semester, and will be instructed by H. S. Seung.

This class counts for a total of 12 credits. This is a graduate-level class.

You can find more information at the 9.641J/8.594J Introduction to Neural Networks site.

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