Numerical Algorithms

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1. rramadassAug 2025
   What you are trying to learn/understand falls under the rubric of Scientific Computing/Numerical Methods/Numerical Analysis/Numerical Algorithms. The mathematics underpinning them is quite wide but mostly Linear Algebra and Calculus. You might find the following useful for your study;

   1) Scientific Computing by Michael Heath - Classic text covering a broad swath of domains and tries to build motivation/intuition before the mathematics (affordable Indian edition available).

   2) Mathematical Principles for Scientific Computing and Visualization by Gerald Farin and Dianne Hansford - Nice overview of needed background. The authors also have a book named Practical Linear Algebra: A Geometry Toolbox which you might find a ideal companion.

   3) Numerical Methods: Fundamentals and Applications by Rajesh Kumar Gupta - A relatively recent book with a really broad coverage of subjects and detailed mathematical expositions (affordable Indian edition available).

   4) Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics by Justin Solomon - Good explanations (affordable Indian edition available). Free ebook available at https://people.csail.mit.edu/jsolomon/

   5) Finally, Mathematics for Physicists: Introductory Concepts and Methods by Alexander Altland and Jan Von Delft is an excellent book to have as a reference. It has three sections viz. Linear Algebra, Calculus and Vector Calculus. The presentation is very precise, does not focus on proofs/lemmas but on concepts and covers a wide swath of important mathematics.

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2. bronxbomber92Dec 2016
   Here's what I've found along the lines of "mathematics for machine learning":

   * DS-GA 1002: Statistical and Mathematical Methods (http://www.cims.nyu.edu/~cfgranda/pages/DSGA1002_fall15/inde...) by Carlos Fernandez-Granda of NYU

   (There is a 2016 version of the course with different lectures notes as well.)

   * Numerical Algorithms (http://people.csail.mit.edu/jsolomon/share/book/numerical_bo...) by Justin Solomon

   * Math for Intelligent Systems, 2016 (https://ipvs.informatik.uni-stuttgart.de/mlr/teaching/maths-...) by Marc Toussaint & Hung Ngo of University Stuttgart

   * Math for Intelligent Systems, 2015 (https://ipvs.informatik.uni-stuttgart.de/mlr/teaching/mathem...) by Nathan Ratliff of University Stuttgart

   * Mathematics for Inference and Machine Learning (http://wp.doc.ic.ac.uk/sml/teaching/mathematics-for-machine-...) by Stefanos Zafeiriou and Marc Deisenroth of Imperial College London

   Most of these are lectures notes. Some are very detailed, but I think there is still space for a completely fleshed out book on the subject.

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