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.
"synopsis" may belong to another edition of this title.
Stephen Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering at Stanford University,California, with courtesy appointments in the Department of Computer Science, and the Department of Management Science and Engineering. He is the co-author of Convex Optimization (Cambridge, 2004), written with Lieven Vandenberghe.
Lieven Vandenberghe is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles, with a joint appointment in the Department of Mathematics. He is the co-author, with Stephen Boyd, of Convex Optimization (Cambridge, 2004).
"About this title" may belong to another edition of this title.
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Hardback. Condition: Fair. 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. It is 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. Seller Inventory # 00107934042
Seller: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condition: Good. Cover is worn/bent. Pages are clean. Seller Inventory # mon0004173682
Seller: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condition: Very Good. Cover and edges may have some wear. Seller Inventory # mon0004084000
Seller: Greener Books, London, United Kingdom
Hardcover. Condition: Used; Very Good. **SHIPPED FROM UK** We believe you will be completely satisfied with our quick and reliable service. All orders are dispatched as swiftly as possible! Buy with confidence! Greener Books. Seller Inventory # 5132184
Quantity: 1 available
Seller: Basi6 International, Irving, TX, U.S.A.
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEOCT25-412107
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Seller Inventory # ABBB-263262
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. 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. A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781316518960
Seller: GoldBooks, Denver, CO, U.S.A.
Hardcover. Condition: new. New Copy. Customer Service Guaranteed. Seller Inventory # 73J99_85_1316518965
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 369449557
Quantity: 1 available
Seller: Books Puddle, Woodside, NY, U.S.A.
Condition: New. 1st edition NO-PA16APR2015-KAP. Seller Inventory # 26376628618