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A Programmer's Introduction to Mathematics by Jeremy Kun 2nd Edition
- A comprehensive guide to mathematics tailored for programmers, with a focus on its applications in machine learning and programming.
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Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisat & Cheng Soon Ong Online Edition
- An online resource that covers essential mathematical concepts essential for machine learning practitioners.
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Calculus for Machine Learning by Stefania Christina & Mehreen Saeed 1st Edition
- Explore calculus from a machine learning perspective, with practical examples and applications.
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Elementary Linear Algebra by Ron Larson 8th Edition
- A comprehensive textbook covering linear algebra concepts, suitable for beginners and advanced learners.
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Introduction to Linear Algebra by Gilbert Strang 6th Edition
- An accessible introduction to linear algebra, widely used in machine learning and data science courses.
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Linear Algebra and Its Applications by David C. Lay, Steven R. Lay & Judi J. McDonald 6th Edition
- A textbook that combines theory with practical applications of linear algebra.
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The Hitchhiker's Guide to Calculus by Michael Spivak Reprint Edition
- An engaging guide to calculus concepts, suitable for both beginners and those looking for a fresh perspective.
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Calculus by Michael Spivak 4th Edition
- A classic textbook that delves deep into calculus theory and applications.
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Calculus, A Complete Course by Robert A. Adams & Christopher Esser 9th Edition
- A comprehensive calculus course with a focus on problem-solving and real-world applications.
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Why Graph Theory Is Cooler than You Thought by Sid Arciadacono Towards Data Science
- Explore the fascinating world of graph theory and its relevance in data science and AI through this engaging blog post.
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Introduction to Graph Machine Learning by Clémentine Fourrier Hugging Face blog
- Delve into the foundations of graph machine learning in this informative blog post.
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What is Graph Theory, and Why Should You Care? by Vegard Flovik KDnuggets
- Discover the practical applications and significance of graph theory in the world of data science and machine learning.
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A Textbook of Graph Theory by R. Balakrishnan & K. Balakrishnan 2nd Edition
- A comprehensive textbook covering the fundamental concepts of graph theory, suitable for both beginners and advanced learners.
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Combinatorics and Graph Theory by John M. Harris, Jeffrey L. Hirst & Michael J. Mossinghoff 2nd Edition
- This textbook offers a detailed exploration of combinatorics and graph theory, essential for those interested in data analysis and network science.
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Introductory Combinatorics by Richard A. Brualdi 5th Edition
- An introductory text that provides a solid foundation in combinatorial mathematics, applicable in various data science and optimization problems.
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An Introduction to Kolmogorov Complexity and Its Applications by Ming Li & Paul Vitányi 4th Edition
- Explore the concept of Kolmogorov complexity and its applications in data compression, information theory, and algorithmic complexity.
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A Survey of Topological Machine Learning Methods by Felix Hensel, Michael Moor & Bastien Rieck 2021 Paper
- This paper surveys the growing field of topological machine learning, offering insights into its principles and applications in data analysis.