02718nam a22005295i 4500 978-3-031-54908-3 DE-He213 20251030112041.0 cr nn 008mamaa 240514s2024 sz | s |||| 0|eng d 9783031549083 978-3-031-54908-3 10.1007/978-3-031-54908-3 doi QA184-205 PBF bicssc MAT002050 bisacsh PBF thema Zizler, Peter. author. (orcid)0000-0003-2611-034X https://orcid.org/0000-0003-2611-034X aut http://id.loc.gov/vocabulary/relators/aut 76794 Linear Algebra in Data Science [electronic resource] / by Peter Zizler, Roberta La Haye. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Birkhäuser, 2024. VIII, 199 p. 23 illus., 9 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Compact Textbooks in Mathematics, 2296-455X 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. Algebras, Linear. 71217 Artificial intelligence Data processing. Computer science Mathematics. 64041 Linear Algebra. 71220 Data Science. Mathematical Applications in Computer Science. 65518 La Haye, Roberta. author. aut http://id.loc.gov/vocabulary/relators/aut 76795 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031549076 Printed edition: 9783031549090 Compact Textbooks in Mathematics, 2296-455X 75270 https://doi.org/10.1007/978-3-031-54908-3 ZDB-2-SMA ZDB-2-SXMS 59844 59844