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Graduate Degree Programs

These course descriptions are not being updated as of August 1, 2016. Current course descriptions are maintained in LionPATH.

Engineering Science (E SC)

E SC 456 (E E 456, EGEE 456) Introduction to Neural Networks (3) Artificial Neural Networks as a solving tool for difficult problems for which conventional methods are not available.

E SC (E E/EGEE) 456 Introduction to Neural Networks (3)

This course is in response to students' needs to learn Artificial Neural Networks (ANN) as a solving tool for difficult problems for which conventional methods are not available. The objective of this course is to give students hands-on experiences in identifying the best types of ANN, plus developing and applying ANN to solve difficult problems. Students will be introduced to a variety of ANN and will use their training skills to solve their own applications. During this course the students will develop a final project, in which they will apply ANN to widely varied problems.

Examples:
I ) students from E E may be interested in applying ANN to solve control problems
II ) students from Material Sciences may be interested in applying ANN to predict the pitting corrosion of components
III ) students from Petroleum Engineering may be interested in applying ANN to characterize the life of a reservoir
IV ) students from Agricultural Engineering may be interested in applying ANN to sort apples automatically, etc


General Education: None
Diversity: None
Bachelor of Arts: None
Effective: Spring 2008
Prerequisite: CMPSC 201 orCMPSC 202;MATH 220

Note : Class size, frequency of offering, and evaluation methods will vary by location and instructor. For these details check the specific course syllabus.

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Course descriptions are stored in LionPATH, the University-wide student information system. Please visit the LionPATH Course Catalog to access current course descriptions. At that point, you will be leaving the University Bulletin website.



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