This book grew out of the graduate and undergraduate courses the authors built and taught at colleges and universities, refined over many semesters as the field changed underneath the syllabus. Both authors split their time between academia and applied research with industry partners; the material here reflects what survived the round trip between the two.
Alexander (Sasha) Apartsin, Ph.D.
Sasha holds a Ph.D. in Computer Science from Tel Aviv University, M.Sc. degrees from the Weizmann Institute and NYU Polytechnic, and a B.Sc. from the Technion.
Before returning to academia he led data science, AI, and research teams across the technology sector: automotive AI, telecommunications, and financial services. That industry stretch shaped a research program he now calls Pragmatic AI: designing methods that hold up when real-world data and conditions depart from clean benchmarks. The current work covers robust perception under degraded inputs, constraint-driven generation with verifiable specifications, and coordination mechanisms for multi-agent systems; the restoration, deployment, and generative chapters of this book (Parts I, III, and IV) draw directly on that line of work.
His teaching focuses on the practical and scientific foundations of modern AI systems. The undergraduate and graduate courses he designed and taught in computer vision, deep learning, generative AI, large language models, and NLP are the immediate ancestor of this book; Part IV in particular follows the arc of his graduate course Generative AI: From VAEs to World Models, and many of the exercises at section ends started life as course assignments.
Yehudit holds a Ph.D. in Mathematical Economics from the Weizmann Institute and an M.Sc. in Game Theory from the Technion, with postdoctoral work in Financial Mathematics at Bar-Ilan University. She has been on the Afeka faculty since 2008.
She founded and directed the M.Sc. Program in Intelligent Systems at Afeka from 2016 to 2023, and in 2024 founded ICSGen.AI, the Afeka Interdisciplinary Center for Social Good and Generative AI. She has led collaborative research projects with defense organizations and industry partners, focusing on intelligent systems applied to healthcare, finance, and signal processing; the applied industry examples that run through every part of this book draw on those collaborations.
Her teaching spans machine learning, data science, optimization, game theory, and decision-making under uncertainty. Her experience designing and leading academic programs in these areas shaped the educational structure and interdisciplinary orientation of this book, particularly the way each part walks the seam between technique and consequence.
The book is produced with a staged pipeline of 42 specialized AI writing agents working under the authors' direction. The authors set the vision, structure, scope, and quality standards, define what each chapter must teach and how the four-part arc fits together, and review the results; the pipeline drafts, critiques, revises, illustrates, and cross-checks the material under that direction. A chapter passes through planning, drafting, structural review, self-containment verification (no concept used before it is taught), clarity and engagement audits, terminology and cross-reference consistency checks across all 39 chapters, visual identity and diagram passes, epigraphs, applied industry examples, exercises, and bibliographies, followed by a final polish and a quality challenge that deliberately attacks each chapter's weakest points.
This way of working has a practical consequence for you as a reader: the book's signature consistency, the recurring callout system, the paired from-scratch and library implementations, the cross-reference arcs that connect convolution to CNNs and denoising to diffusion, is not a style guide that authors tried to remember. It is enforced mechanically, chapter after chapter, by agents whose only job is to check it. The full roster, one card per agent, is in Appendix F: Agents That Helped to Write This Book. A book produced this way is reviewed many more times than any single human could manage, and still, errors can survive any process. Where the text and reality disagree, reality wins; corrections are folded into subsequent revisions of the text.
Note: If the book helps
The book is independently published. If a chapter saved you a day of debugging or unlocked a project, a sentence about which chapter helped is more useful to other readers than a five-star rating.
