Zizler, Peter. creator author. aut http://id.loc.gov/vocabulary/relators/aut La Haye, Roberta. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access VIII, 199 p. 23 illus., 9 illus. in color. online resource. 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. by Peter Zizler, Roberta La Haye. Algebras, Linear Artificial intelligence Data processing Computer science Mathematics Linear Algebra Data Science Mathematical Applications in Computer Science QA184-205 9783031549083 https://doi.org/10.1007/978-3-031-54908-3 https://doi.org/10.1007/978-3-031-54908-3 240514 20251030112041.0 978-3-031-54908-3