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Biologically based machine learning paradigms: an introductory course

Published: 01 March 1992 Publication History

Abstract

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.

References

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cover image ACM Conferences
SIGCSE '92: Proceedings of the twenty-third SIGCSE technical symposium on Computer science education
March 1992
332 pages
ISBN:0897914686
DOI:10.1145/134510
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 March 1992

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