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Institution:
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Northeastern University
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Subject:
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Description:
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Uses the Gauss-Jordan elimination algorithm to analyze and find bases for subspaces such as the image and kernel of a linear transformation. Covers the geometry of linear transformations: orthogonality, the Gram-Schmidt process, rotation matrices, and least squares fit. Examines diagonalization and similarity, and the spectral theorem and the singular value decomposition. Is primarily for math and science majors; applications are drawn from many technical fields. Computation is aided by the use of software such as Maple or MATLAB, and graphing calculators.
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Credits:
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4.00
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Credit Hours:
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Prerequisites:
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Corequisites:
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Exclusions:
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Level:
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Instructional Type:
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Lecture
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Notes:
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Additional Information:
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Historical Version(s):
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Institution Website:
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Phone Number:
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(617) 373-2000
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Regional Accreditation:
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New England Association of Schools and Colleges
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Calendar System:
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Semester
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