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001 978-3-031-54908-3
003 DE-He213
005 20251030112041.0
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020 _a9783031549083
_9978-3-031-54908-3
024 7 _a10.1007/978-3-031-54908-3
_2doi
050 4 _aQA184-205
072 7 _aPBF
_2bicssc
072 7 _aMAT002050
_2bisacsh
072 7 _aPBF
_2thema
100 1 _aZizler, Peter.
_eauthor.
_0(orcid)0000-0003-2611-034X
_1https://orcid.org/0000-0003-2611-034X
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976794
245 1 0 _aLinear Algebra in Data Science
_h[electronic resource] /
_cby Peter Zizler, Roberta La Haye.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Birkhäuser,
_c2024.
300 _aVIII, 199 p. 23 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCompact Textbooks in Mathematics,
_x2296-455X
520 _aThis 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.
650 0 _aAlgebras, Linear.
_971217
650 0 _aArtificial intelligence
_xData processing.
650 0 _aComputer science
_xMathematics.
_964041
650 1 4 _aLinear Algebra.
_971220
650 2 4 _aData Science.
650 2 4 _aMathematical Applications in Computer Science.
_965518
700 1 _aLa Haye, Roberta.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976795
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031549076
776 0 8 _iPrinted edition:
_z9783031549090
830 0 _aCompact Textbooks in Mathematics,
_x2296-455X
_975270
856 4 0 _uhttps://doi.org/10.1007/978-3-031-54908-3
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c59844
_d59844