Linear algebra and optimization for machine Learning: a textbook
Aggarwal, Charu C
Linear algebra and optimization for machine Learning: a textbook - 1st - Switzerland: Springer, 2020 - xxi, 498p.: 6x11x2; Paperback
This 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
9783030403461
Mathematics; Optimization; Linear systems; Machine learning; Matrix factorization; Linear algebra
512.5 / AGG/LIN
Linear algebra and optimization for machine Learning: a textbook - 1st - Switzerland: Springer, 2020 - xxi, 498p.: 6x11x2; Paperback
This 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
9783030403461
Mathematics; Optimization; Linear systems; Machine learning; Matrix factorization; Linear algebra
512.5 / AGG/LIN