000			01540nam a22001937a 4500
005			20241211170031.0
008			241211b |||||||| |||| 00| 0 eng d
020			_a9783030403461
040			_cNational Institute of Technology Goa
082			_a512.5 _bAGG/LIN
100			_aAggarwal, Charu C
245			_aLinear algebra and optimization for machine Learning: a textbook
250			_a1st
260			_aSwitzerland: _b Springer, _c 2020
300			_axxi, 498p.: 6x11x2; Paperback
520			_aThis textbook introduces linear algebra and optimization in the context of machine learning. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields
650			_2Mathematics _aMathematics; Optimization; Linear systems; Machine learning; Matrix factorization; Linear algebra
942			_2ddc _cBK _n0
999			_c5159 _d5159