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Course Title[Course Code]:Theory of Computation[SCC 433]

Faculty: Computers and Artificial Intelligence
Department: Computer Science
Program: Computer Science
Compulsory / Elective:Compulsory
Undergraduate(Forth Year-Second Semester)
Lecture:( 3 ) Practical / Clinical:( - ) Tutorial:( 2 )

Course Description:
The course aims at introducing the Introduction and a historical review: Overview of neurocomputing, history of neurocomputing. Neural network concepts: Basic definition, connections, processing elements. Learning laws: Self-adaptation equations, coincidence learning, performance learning, competitive learning, filter learning, spatio-temporal learning. Associative networks: Data transformation structures, Linear association network, learn matrix network, recurrent associative networks. Mapping networks: Multilayer data transformation structures, the mapping implementation problem, Kolmogorov’s theorem, the back-propagation neural network, self-organizing map, counter propagation network. Spatiotemporal, stochastic, and hierarchical networks: Saptiotemporal pattern recognizer neural network, the Boltzman machine network, and the neurocognition network.