May 1, 2024
Updated May 8, 2025
24 minute read
An Introduction to Vectors
Vectors are fundamental mathematical objects that possess both magnitude (or length) and direction. Unlike scalar quantities, which are fully described by a single number (like temperature or mass), vectors require more information to define them completely. They are often visualized as arrows pointing from an initial point to a terminal point, with the length of thearrow representing the magnitude and the orientation of the arrow indicating the direction. This seemingly simple concept forms the bedrock of numerous fields, enabling the description and analysis of a wide array of physical phenomena and abstract systems.
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Reading list
We've selected 29 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Vectors.
Is widely recommended for a first course in linear algebra, providing a solid foundation in the fundamental concepts of vectors, matrices, and linear equations. It emphasizes understanding and applying concepts with a focus on practical applications, making it suitable for students across disciplines. It is commonly used as a textbook at the undergraduate level.
Provides an introduction to linear algebra with a strong emphasis on applications in data science, engineering, and other fields. It focuses on vectors, matrices, and least squares, making it highly relevant for students and professionals who need to apply linear algebra to solve real-world problems.
This textbook provides a balance of theory and applications, making it a popular choice for undergraduate linear algebra courses. It covers essential topics including vectors, matrix operations, and eigenvalues, with a clear writing style and numerous examples and exercises. It valuable reference and learning resource.
Provides a comprehensive and rigorous treatment of vector analysis. It great resource for anyone who wants to learn more about the mathematical foundations of vectors. Kemmer was a physicist and mathematician, who worked on theoretical physics.
This recent book by Gilbert Strang focuses on the intersection of linear algebra and data science. It explores how linear algebra concepts, including vectors, are applied in machine learning and data analysis. It is suitable for students and professionals interested in the practical applications of linear algebra in the age of data.
Offers a contemporary introduction to linear algebra with a strong emphasis on geometric intuition and connections to various applications. It is suitable for undergraduate students and provides a good balance between theory, computation, and application of vectors and linear algebra concepts.
Offers a more abstract and theoretical approach to linear algebra, focusing on vector spaces and linear transformations without relying heavily on determinants initially. It is well-suited for students seeking a deeper understanding of the underlying theory and is often used for a second course in linear algebra at the undergraduate or early graduate level. The fourth edition is an open access publication.
Focuses on the applications of linear algebra, including a strong emphasis on vectors and their use in solving problems in various disciplines. It is suitable for undergraduate and graduate students interested in the practical aspects of linear algebra.
Provides a comprehensive overview of vectors and tensors and their applications in fluid mechanics. It great resource for anyone who wants to learn more about the mathematical and physical applications of vectors and tensors in fluid mechanics.
Provides a comprehensive overview of vectors and their applications in physics and engineering. It covers a wide range of topics, including vector algebra, vector calculus, and vector fields. This book great resource for anyone who wants to learn more about the practical applications of vectors.
Offers a concise and practical approach to linear algebra, connecting computational techniques with geometric interpretations and theoretical foundations. It is suitable for university-level students and includes applications in various fields. It can serve as a helpful reference and review tool.
Focuses on the computational aspects of linear algebra, which are crucial for professionals in many fields. It covers algorithms and techniques for solving linear systems, eigenvalue problems, and singular value decomposition, all of which heavily rely on vector and matrix operations.
This influential book in the field of statistical learning and data mining heavily utilizes linear algebra concepts. It showcases the application of vectors and matrices in statistical modeling and valuable resource for graduate students and professionals in data science and machine learning.
This comprehensive book covers various mathematical methods used in physics, including a significant portion on vectors, vector spaces, and linear algebra. It is an essential reference for undergraduate and graduate physics students, illustrating the practical application of vector concepts in physical problems.
Covers the fundamental concepts of deep learning, where linear algebra, particularly vector and matrix operations, plays a crucial role. It demonstrates the application of vectors in a contemporary field and is suitable for graduate students and professionals interested in the mathematical underpinnings of AI and machine learning.
While not solely focused on vectors, this comprehensive physics textbook provides a strong foundation in the application of vectors in mechanics and other areas of physics. It is widely used in undergraduate physics programs and is essential for understanding how vectors are used to model physical phenomena.
Focuses on vector calculus, building upon the foundational understanding of vectors. It covers topics such as vector fields, line integrals, and surface integrals, which are essential in physics and engineering. It is suitable for undergraduate students who have completed a basic linear algebra course.
Part of the Schaum's Outline series, this book offers a vast collection of solved problems in linear algebra, covering a wide range of topics including vectors. It is an excellent supplementary resource for students to practice and solidify their understanding of the concepts.
Delves into convex optimization, a field with significant applications in engineering, finance, and machine learning. Linear algebra, including vector spaces and operations, fundamental tool used throughout the book. It is suitable for graduate students and researchers and serves as a key reference in the field.
For more information about how these books relate to this course, visit:
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