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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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ALL 6822
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Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.
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- Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
| Publisher | Cambridge University Press |
| Publication date | May 16, 2024 |
| Edition | 1st |
| Language | English |
| Print length | 452 pages |
| ISBN-10 | 1009418149 |
| ISBN-13 | 978-1009418140 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 6.75 x 1.25 x 9.5 inches (17.1 x 3.2 x 24.1 cm) |
Product Description
Linear Algebra for Data Science, Machine Learning, and Signal Processing
Linear Editorial Review
Linear Algebra for Data Science, Machine Learning, and Signal Processing is a comprehensive guide that delves into the mathematical foundations essential for understanding complex algorithms in data science and signal processing. Published by Cambridge University Press, it spans 452 pages and features a user-friendly approach for learners. The book's first edition, set to release on May 16, 2024, presents linear algebra concepts in a practical context, making it incredibly relevant for professionals in the tech field. Suitable for readers looking to bolster their machine learning expertise, the book is written in English and offers clear insights into leveraging linear algebra effectively in real-world scenarios.
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Pros
- Comprehensive coverage of linear algebra concepts
- Practical applications in data science
- User-friendly layout and organization
- Ideal for machine learning enthusiasts
- Relevant examples for signal processing
Cons
- May be challenging for complete beginners
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ALL 6822
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Features & Benefits
- Engaging textbook designed for upper-level undergraduates and first-year graduate students.
- Covers both basic and advanced matrix topics relevant to data science and machine learning.
- Includes 200+ multiple-choice questions for interactive classroom learning.
- Features practical exercises with solutions available for instructors.
- Encourages active learning through 'explore' questions and practical Julia code examples.
- Offers a suite of computational notebooks for hands-on learning experiences.
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