Mathematics for Machine Learning

Great book. Perfect for someone with High school level Math background who wants to break into machine learning and not just be a passive user of the technology. I would love to see a second version of this book that goes beyond traditional methods. You get to learn meanings of all the jargons being thrown around (like PCA, L2 norm, rank, basis, etc etc.) by machine learning engineers and develop your own understanding from first principles.

You can train all the models that you want but if you don't understand its building blocks, that is, mathematics, you will be lost. This book is a great resource which helps you ease into the world of mathematics behind machine learning in a very simple way.

I have just received this book hours ago. It is paperback. I breezed through book with skimreading at first. Simple illustrations with few colours — VERY HELPFUL. The page layout is perfect, very easy on my eyes! I could read quickly, as the text is not too cluttered. I have learned maths fast. Thank you, authors. Moreover, I absolutely love the 4cm margins at the outer edges of pages, as I like pencilling my notes in blank spaces or place sticky notes there. Helpful footnotes in the margins.

The book arrived with a small glue issue on the first pages, but nothing that affects reading or use. The content itself is excellent, clear, well-structured, and focused. It’s not a basic maths book though; it assumes you already have some background in algebra, statistics, and basic calculus. If you already understand those foundations and want to connect them to machine learning, this is a great choice.

Wide ranging with basic principles of foundation in maths and machine learning

It's a very good (and maybe only) resource for someone who's starting on the field of machine learning and is trying to understand the underlying mathematics.

Good for ml enthusiast's. All the important maths subjects are covered

I have a PhD in ML and a CS background. My stats knowledge is lacking so I was hoping this book could help me get a better understanding of the core foundational concepts in ML. In the first few chapters (Part 1 of the book) there is a lot of skimming over the math which makes it difficult for me to learn. I have to spend more time looking at other sources to fill in the blanks. Part 2 is a lot easier to read. I enjoyed these chapters a lot more.

Nachdem ich vor 25 Jahren Informatik studiert habe und dort bereits "Neuronale Netze" (feed-forward back-propagation) kennengelernt hatte, wollte ich, motiviert durch den Hype der aktuellen AI (insbesondere machine learning sowie deep learning) mehr darüber lesen. Daher zunächst das "Standardwerk" (Titel "Deep Learning") gekauft. Die dort enthaltene Mathematik ist, meines Erachtens, so stark ver-klausuliert und auch von der Notation her schwer zu lesen, dass ich dieses Buch hier "Mathematics for Machine Learning" gekauft habe: Ich muss sagen/schreiben: Das ist die BESTE Darstellung der verschiedenen mathematischen Themenbereiche (Vektoren, Matrizen, Lineare Algebra, Wahrscheinlichkeitsrechnung, u.s.w.), die ich als Praktiker der Informatik je gesehen habe. Sehr gut verständlich (mit dem math. Grundwissen eines Informatikers), sehr tolle praxis-bezogene Beispiele zu den mathematischen Verfahren. Darüber hinaus in einem hervorragenden Englisch geschrieben, das wirklich Freude macht, es zu lesen. Ich denke, dass jeder, der sich intensiv mit Machine Learning auseinandersetzen möchte, hier sowohl ein Lehrwerk als auch ein Nachschlagewerk erhält. Übungen mit Lösungen (auf github) runden dieses Buch ab. Ich bin begeistert!!!

Le basi matematiche di questo libro non sono da super specialisti. Ma anche per chi è un ricercatore, questo libro offre un approccio diverso su molti temi standard, facendoti guardare a cose che conosci bene da un punto di vista inaspettato

Les bases mathématiques et analyse numériques de niveau Master1 (Bac+4). Agréable à avoir en format papier. + accès au site web pour suivre les quelques coquilles. Simple regret : impossible d'accéder aux corrections des nombreux exercices sans être un enseignant dans une faculté.

This is a very structured approach to gain a strong grasp of the mathematical fundamentals required for machine learning. If you pair this up with "Understanding Machile Learning: From Theory to Algorithms" by Shai Ben-David and Shai Shalev Shwartz, then that's a clear winner combo for ML theory.

Good book