Linear Algebra in Data Science
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Beschreibung
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.
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.
Explores applications of linear algebra in data science, showing readers how the two are connected Offers exercises that escalate in complexity, many of which incorporate MATLAB Includes practice projects that show real-world applications of the material covered in a standard linear algebra course
Autor*in
Peter Zizler
Themen in »Linear Algebra in Data Science«
Linear algebra Data science Neural networks Wavelet transform Linear algebra Singular value decomposition Quaternions Haar wavelets Frequency filtering Orthogonal decompositions Skew projections Moore-Penrose inverse LU factorization QR factorization
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"Linear algebra in data science is a remarkable resource that seamlessly connects theoretical principles with practical applications. Zizler and La Haye have created an insightful and engaging guide that caters to a broad audience, from students to seasoned professionals. ... this book empowers readers to apply linear algebra in diverse data science contexts. It is a must-have for anyone seeking to deepen their understanding of the mathematical frameworks driving modern data science." (Pagadala Usha, Computing Reviews, June 4, 2025)
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