A new graduate course on neural networks

Author

Braham, Rafik

Author_Institution

Dept. of Comput. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia

Volume

37

Issue

3

fYear

1994

fDate

8/1/1994 12:00:00 AM

Firstpage

277

Lastpage

280

Abstract

A new neural network course at the graduate level is described. The course is offered at the computer engineering department but is intended for a wider audience. It is currently taken as a technical elective by master´s students at King Fahd University of Petroleum and Minerals (KFUPM). The course´s main topics are: foundations of neural computation; architectures of neural networks; learning; dynamics; feedback models; VLSI implementations; parallel processing; and fault-tolerance. The course is not a survey of neural network models, learning algorithms, etc. It is instead an in-depth presentation of neural computing principles with illustrations from few but diverse models

Keywords

computer science education; educational courses; fault tolerant computing; learning (artificial intelligence); neural nets; parallel architectures; parallel processing; VLSI; architectures; computation; computer engineering; dynamics; education courses; fault-tolerance; feedback models; graduate; learning; neural network; parallel processing; students; Computer architecture; Computer networks; Concurrent computing; Fault tolerance; Minerals; Neural networks; Neurofeedback; Parallel processing; Petroleum; Very large scale integration;

fLanguage

English

Journal_Title

Education, IEEE Transactions on

Publisher

ieee

ISSN

0018-9359

Type

jour

DOI

10.1109/13.312138

Filename

312138