Linear Algebra and Optimization for Machine Learning

Name: Linear Algebra and Optimization for Machine Learning
Brand: Springer, Berlin
SKU: 9783031986185
Price: 297.52 RON
Availability: InStock

A Textbook

Autor: Charu Aggarwal

Limbă:

Legare: Copertă tare

Editura: Springer, Berlin

Disponibilitate: În depozitul extern

Expediem în 10-13 zile

297.52 lei

This textbook is the second edition of the linear algebra and optimization book that was published i...

Informații despre carte

Autor

Charu Aggarwal

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2025

Pagini

650

EAN

9783031986185

Enbook ID

48984776

Editura

Springer, Berlin

Greutate

1439

Dimensiuni

178 x 254

Descriere completă

This textbook is the second edition of the linear algebra and optimization book that was published in 2020. The exposition in this edition is greatly simplified as compared to the first edition. The second edition is enhanced with a large number of solved examples and exercises. 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 application-centric 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.

It is common for machine learning practitioners to pick up missing bits and pieces of linear algebra and optimization via osmosis while studying the solutions to machine learning applications. However, this type of unsystematic approach is unsatisfying because the primary focus on machine learning gets in the way of learning linear algebra and optimization in a generalizable way across new situations and applications. Therefore, we have inverted the focus in this book, with linear algebra/optimization as the primary topics of interest, and solutions to machine learning problems as the applications of this machinery. In other words, the book goes out of its way to teach linear algebra and optimization with machine learning examples. By using this approach, the book focuses on those aspects of linear algebra and optimization that are more relevant to machine learning, and also teaches the reader how to apply them in the machine learning context. As a side benefit, the reader will pick up knowledge of several fundamental problems in machine learning. At the end of the process, the reader will become familiar with many of the basic linear-algebra- and optimization-centric algorithms in machine learning. Although the book is not intended to provide exhaustive coverage of machine learning, it serves as a technical starter for the key models and optimization methods in machine learning. Even for seasoned practitioners of machine learning, a systematic introduction to fundamental linear algebra and optimization methodologies can be useful in terms of providing a fresh perspective.

The chapters of the book are organized as follows.

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Linear Algebra and Optimization for Machine Learning

A Textbook

Informații despre carte

Descriere completă

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