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Linear Algebra in Data Science [electronic resource] / by Peter Zizler, Roberta La Haye.

By:

Zizler, Peter [author.]

Contributor(s):

Material type:

TextSeries: Compact Textbooks in MathematicsPublisher: Cham : Springer International Publishing : Imprint: Birkhäuser, 2024Edition: 1st ed. 2024Description: VIII, 199 p. 23 illus., 9 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783031549083

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No titleLOC classification:

QA184-205

Online resources:

Resursa online

In: Springer Nature eBookSummary: This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.

Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)

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This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.

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