A first undergraduate course in neural networks

Abunawass, Adel M.; Bukhres, Omar; Fisher, Theresia G.; Magel, Kenneth

doi:10.1145/319059.323464

SIGCSE Bull.

1990

DOI: 10.1145/319059.323464

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A first undergraduate course in neural networks

Adel M. Abunawass

Omar Bukhres

Theresia G. Fisher

et al.

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1992

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Biologically based machine learning paradigms

Abunawass

1992

Proceedings of the Twenty-Third SIGCSE Technical Symposium on Computer Science Education

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This paper describes an introductory course on biologically based sub-symbolic machine learning paradigms. Specifically, this paper covers Artificial Neural Networks, Genetic Algorithms and Genetics-Based Machine Learning. It provides the structure, motivation, content, texts and tools for the course. This course is suitable for an upper division undergraduate level course or as an introductory graduate course. The paper includes a section on bibliographical references to aid the instructor in preparing for this course.

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Biologically based machine learning paradigms

Abunawass

1992

Proceedings of the Twenty-Third SIGCSE Technical Symposium on Computer Science Education

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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A first undergraduate course in neural networks

Cited by 1 publication

References 8 publications

Biologically based machine learning paradigms

Biologically based machine learning paradigms

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