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Linear Algebra and Optimization for Machine Learning: A Textbook
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This textbook introduces linear algebra and optimization in the context of machine learning with numerous exercises and examples provided. Advanced undergraduate students, graduate-level students, and professors can use this textbook.
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Product Details
- Introduces linear algebra and optimization within the context of machine learning
- Includes examples and exercises with a solution manual for teaching instructors
- Targeted at graduate level students, professors in computer science, mathematics, and data science, as well as advanced undergraduate students
- Chapters organized to cover basics of linear algebra with applications to singular value decomposition, matrix factorization, kernel methods, and graph analysis
- Covers optimization problems in machine learning such as least-squares regression, support vector machines, logistic regression, recommender systems, and dimensionality reduction
- Focuses on the most relevant aspects of linear algebra and optimization for machine learning applications
| Publisher | Springer |
| Publication date | May 13, 2020 |
| Edition | 1st ed. 2020 |
| Language | English |
| Print length | 516 pages |
| ISBN-10 | 3030403432 |
| ISBN-13 | 978-3030403430 |
| Item Weight | 2.3 pounds (1.04 kg) |
| Dimensions | 7.01 x 1.13 x 10 inches (17.8 x 2.9 x 25.4 cm) |
Who Should Buy?
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Graduate Students
Ideal for graduate students specializing in machine learning or related fields requiring in-depth knowledge of linear algebra and optimization.
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Data Scientists
Beneficial for data scientists seeking to enhance their understanding of mathematical foundations crucial for effective machine learning models.
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Educators and Researchers
Useful resource for educators and researchers teaching advanced concepts in linear algebra and optimization within machine learning contexts.
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Beginners in Math
Not suitable for those new to linear algebra or optimization, as it assumes a certain level of prior knowledge.
Product Description
Linear Algebra and Optimization for Machine Learning: A Textbook
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Linear Editorial Review
Linear Algebra and Optimization for Machine Learning: A Textbook offers an in-depth exploration of linear algebra concepts and optimization techniques critical for machine learning. The book, published by Springer in May 2020, spans 516 pages and includes essential topics like Eigenvectors, Eigendecomposition, and Principal Component Analysis. Many readers appreciate the mathematically rigorous approach taken by Aggarwal, highlighting the clear progression to advanced topics. However, some reviewers found the book challenging due to a lack of worked examples, diagrams, and practical exercises, which detracted from their understanding of the material. Despite these concerns, the comprehensive coverage of specialized topics makes this textbook a valuable resource for those delving into machine learning.
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Pros
- Clear explanations of complex concepts
- Covers specialized topics in depth
- Rich set of challenging exercises
- Well-structured progression of topics
- Useful background for understanding machine learning
Cons
- Some may find the content dense and overwhelming
Product Price History
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Features & Benefits
- Linear Algebra and Optimization for Machine Learning is a textbook that teaches linear algebra and optimization in the context of machine learning
- The book has numerous exercises and examples
- This book is suitable for advanced undergraduate and graduate-level students and professors
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