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Sinopse
1. 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. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.
2. 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. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks.
Ficha Técnica
Especificações
ISBN | 9783030403430 |
---|---|
Subtítulo | A TEXTBOOK |
Pré venda | Não |
Peso | 540g |
Autor para link | AGGARWAL CHARU C. |
Livro disponível - pronta entrega | Sim |
Dimensões | 23 x 16 x 1 |
Idioma | Inglês |
Tipo item | Livro Importado |
Número de páginas | 516 |
Número da edição | 1ª EDIÇÃO - 2020 |
Código Interno | 1009799 |
Código de barras | 9783030403430 |
Acabamento | PAPERBACK |
Autor | AGGARWAL, CHARU C. |
Editora | SPRINGER |
Sob encomenda | Não |