9.641J Introduction to Neural Networks
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.
9.641J will be offered this semester (Spring 2019). It is 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.
© Copyright 2015 Yasyf Mohamedali