Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
📄 Viewing lite version
Full site ›
Book Details
Author(s)Boyd, Stephen
PublisherCambridge University Press
ISBN / ASIN1316518965
ISBN-139781316518960
AvailabilityIn Stock.
Sales Rank31,358
MarketplaceUnited States 🇺🇸
Description ▲
This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.
Similar Products ▼
- Linear Algebra and Learning from Data
- The Hundred-Page Machine Learning Book
- Introduction to Deep Learning (The MIT Press)
- A Programmer's Introduction to Mathematics
- Neural Networks and Deep Learning: A Textbook
- Convex Optimization, With Corrections 2008
- Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
- Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence
- High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics)
- Daily Coding Problem: Get exceptionally good at coding interviews by solving one problem every day