Essential Math for CS
An enjoyable and readable textbook on mathematics, LA4CS introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science.
The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.
Various Editions of LA4CS available:
Welcome to the Wonderful World of Linear Algebra!
In today’s age of machine learning and artificial intelligence, Linear Algebra is the branch of mathematics that holds the most relevance to computing.
Why learn Linear Algebra in computer science and data analytics? Its role is similar to that of the alphabet or vocabulary or grammar in learning a language. If we want to be a writer, for instance, we have to be good at all these aspects of the language of our choice. Having these background skills alone is not enough; it will not make us a writer. But what is absolutely certain is that without these skills, we will never be a writer, not a good one at any rate.
Linear Algebra, in much the same way, is really the basic backdrop of several of the pivotal numerical algorithms in computer science and machine learning.
Lecture Videos (from the course for which this book was written)
- Chapter 1: Functions, Equations and Linearity (August 20, 2021)
- Chapter 2: Vectors, Matrices and Their Operations (August 27, 2021)
- Chapter 3: Transposes and Determinants (Sept 03, 2021)
- Chapter 4: Gaussian Elimination (Sept 10, 2021)
- Chapter 5: Ranks and Inverses of Matrices. Gauss-Jordan Elimination (Sept 17, 2021)
- Chapter 6: Vector Spaces, Basis and Dimensions (Sept 23, 2021)
- Chapter 7: Change of Basis, Orthogonality and Gram-Schmidt (Sept 30, 2021)
- No Chapter 8: Recess Week
- Chapter 9: The Four Fundamental Spaces (Oct 14, 2021)
- Chapter 10: Projection, Least Squares and Linear Regression (Oct 21, 2021)
- Chapter 11: Eigenvalue Decomposition and Diagonalization (Oct 28, 2021)
- Chapter 12: Special Matrices, Similarity and Algorithms (Nov 4, 2021)
- Chapter 13: Singular Value Decomposition (Nov 11, 2021)
Manoj Thulasidas is an Associate Professor of Computer Science (Education) who teaches Data Analytics and Linear Algebra to undergraduate students of computer science and information systems at Singapore Management University.
His other works include The Unreal Universe, an inquiry into the philosophical underpinnings of physics (from his career as a physicist at CERN in Geneva) and Principles of Quantitative Development, a practitioner’s guide to the lucrative profession of quantitative finance (from his experiences as a quant in Singapore).
Published Articles, Columns…
The Unreal Universe
Principles of Quantitative Development
Linear Algebra for Computer Science
An anthology of the internet kind, Unreal Blog.
Visit My Blog!
The primary objective of Unreal Blog is to entertain you with quality writing. Accidentally discovered and carefully cultivated, my writing skill has seen a measure of success in newspapers and magazines around the world. Unreal Blog promote a skeptical worldview and makes you look at everything from a slightly different perspective. It really does aim to change the way the reader looks at the world!