Kirk Borne

Here is my review: 𝗧𝗵𝗲 𝗦𝗲𝘁 𝗢𝗳 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗧𝗵𝗮𝘁 𝗟𝗲𝗮𝗿𝗻 𝗙𝗿𝗼𝗺 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 This massive book is incredible, with its comprehensive coverage of numerous fields of mathematics and their intersection with the world of AI, data science, and machine learning (AI+DSML). I remember the very first time that I encountered machine learning. This was 20+ years ago, and that was already after 20+ years of being drenched in advanced mathematics as an astrophysicist. That first encounter of mine with ML was this definition: "Machine learning is the set of mathematical algorithms that learn from experience" (slightly paraphrased from the original quote by Tom Mitchell, CMU). That definition surprised me, confused me, motivated me, and changed the course of my career from astrophysics into AI+DSML. This book by Tivadar Danka captures the full meaning of that definition. The book covers thoroughly the many areas and domains of mathematics through which patterns in data are detected, described, learned, and recognized - all for the benefit of powering ML and AI algorithms, applications, and aspirations. This book will motivate you, surprise you, and inspire you in many ways, no matter what level of mathematics has (or has not) already propelled your career journey. There is room for all of us to grow. This is a great book, worthy to sit on everyone's desktop, ready to help you explore and exploit the full set of mathematical algorithms that learn from experience. The book is accompanied by a rich GitHub code repository of Jupyter notebooks. Learn by doing! Do by learning! Disclosure: the publisher provided me with a free review copy of the book.