| 书目名称 | Linear Algebra and Optimization for Machine Learning |
| 副标题 | A Textbook |
| 编辑 | Charu C. Aggarwal |
| 视频video | |
| 概述 | First textbook to provide an integrated treatment of linear algebra and optimization with a special focus on machine learning issues.Includes many examples to simplify exposition and facilitate in lea |
| 图书封面 |  |
| 描述 | .This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:.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: M |
| 出版日期 | Textbook 2020 |
| 关键词 | Linear Algebra; Optimization; Machine Learning; Deep Learning; Neural Networks; Dynamic Programming; Suppo |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-40344-7 |
| isbn_softcover | 978-3-030-40346-1 |
| isbn_ebook | 978-3-030-40344-7 |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 19:11:31
