Title
Linear Algebra and Optimization for Machine Learning: A Textbook,Used
Delivery time: 8-12 business days (International)
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, kernelbased 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 optimizationcentric machine learning is leastsquares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Leastsquares 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.A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other applicationcentric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.
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We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics does 'Linear Algebra and Optimization for Machine Learning' cover? A: The textbook covers linear algebra and optimization with a focus on their applications in machine learning, including topics such as singular value decomposition, least-squares regression, and optimization in computational graphs.
- Q: Who is the target audience for this textbook? A: This textbook is aimed at graduate students and professors in computer science, mathematics, and data science, as well as advanced undergraduate students interested in machine learning.
- Q: Is there a solution manual available for the exercises in the book? A: Yes, a solution manual for the exercises at the end of each chapter is available specifically for teaching instructors.
- Q: How is the content structured in this textbook? A: The content is organized into chapters that focus on linear algebra and optimization, with examples and exercises integrated throughout to enhance understanding.
- Q: What makes this textbook different from other linear algebra books? A: This textbook differentiates itself by tightly integrating linear algebra methods with practical examples from machine learning, making it particularly relevant for those in the field.
- Q: Are there exercises included in the textbook? A: Yes, the textbook includes numerous examples and exercises throughout its chapters to reinforce learning and application of the concepts presented.
- Q: What edition is this textbook? A: This is the 1st edition of 'Linear Algebra and Optimization for Machine Learning', published on May 13, 2020.
- Q: What is the condition of the book? A: The book is offered in new condition.
- Q: How many pages does the textbook have? A: The textbook contains 516 pages.
- Q: What binding is used for this textbook? A: The textbook is available in hardcover binding.