000			01505nam a22001937a 4500
005			20241116143743.0
008			241116b |||||||| |||| 00| 0 eng d
020			_a9798886130775
082			_a512.5 _bHAV
100			_aHaviv, Moshe _918670
245			_aLinear algebra for data science
260			_bWorld Scientific Publishing _aSingapore _c2024
300			_axii, 244 p.
365			_aINR _b1695.00
520			_aThis book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some applications to data science along the way. The book comes with two parts, one on vectors, the other on matrices. The former consists of four chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram–Schmidt process, linear functions. The latter comes with eight chapters: matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters. (https://www.worldscientific.com/worldscibooks/10.1142/13408#t=aboutBook)
650			_aLinear algebra _9805
650			_aData science
942			_cBK _2ddc
999			_c7768 _d7768