{"product_id":"9783032249531","title":"Modern Machine Learning and Pattern Recognition","description":"\u003ch1\u003eModern Machine Learning and Pattern Recognition\u003c\/h1\u003e\u003ch3\u003eDjamel Bouchaffra\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp class=\"m-5056960495410490321my-2\"\u003e\u003cem\u003e\u003cspan style=\"font-family: 'Aptos',sans-serif; mso-bidi-font-family: Aptos;\"\u003eModern Machine Learning and Pattern Recognition\u003c\/span\u003e\u003c\/em\u003e presents a rigorous, comprehensive exploration from classical learning paradigms to the latest deep architectures and large language models. Integrating supervised, unsupervised, self-supervised, and reinforcement learning with modern neural network design, the book offers a unified view of machine learning and pattern recognition grounded in statistical learning theory and optimization. Through a progression of chapters, readers move from foundations and multilayer perceptrons to convolutional and recurrent networks, generative adversarial models, and transformer-based large language models.\u003c\/p\u003e\r\n\u003cp\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Aptos',sans-serif; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-bidi-font-family: Aptos; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;\"\u003eA special feature of this text is its combination of theoretical depth with extensive practice-oriented material, including many exercises, Python-based projects, and real-world case studies that bridge mathematical analysis with implementation and experimentation. Beyond just standard architectures, the book introduces original coalitional neural models with energy-based foundations, drawing on statistical physics, game theory, and random matrix theory to analyze and redesign deep networks at a fundamental level. It concludes with dedicated chapters on the ethical and social implications of large-scale models and on emerging research directions such as topological datat analysis, meta-reasoning in LLMs, and causal inference: helping readers connect core techniques to current debates and future developments in AI. \u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"m-5056960495410490321my-2\"\u003eMeant for advanced undergraduates, graduate students, researchers, and professionals, this single-author monograph provides a coherent and pedagogically structured treatment suitable for classroom adoption, self-study, and reference. Readers are equipped not only to understand existing models, but also to engage with ongoing research on interpretability, robustness, and the next generation of learning architectures.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt;\"\u003e\u003cstrong\u003eDr. Djamel Bouchaffra\u003c\/strong\u003e is an Associate Researcher at the DAVID Laboratory, Université Paris-Saclay (UVSQ Campus), France. His research focuses on artificial intelligence, neural networks, and pattern recognition, with particular emphasis on the theoretical foundations of deep learning and emerging interdisciplinary connections between AI, statistical physics, and game theory.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt;\"\u003eHe previously served as Professor of Computer Science and Engineering at Oakland University, Michigan, USA, where he taught courses in artificial intelligence, pattern recognition, and soft computing. In recognition of his teaching excellence, he received both the University Teaching Excellence Award and the School of Engineering Teaching Excellence Award in 2004. He also served as an evaluator for NASA, contributing expertise in statistical data analysis for astrophysics and participating in scientific meetings in Washington, D.C.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt;\"\u003eDr. Bouchaffra has held Visiting Professor positions at Sorbonne Paris Nord University (UMR CNRS 7030) in 2010, 2011, 2024, and 2026. Over the course of his career, he has published extensively in leading international journals and conferences. He previously worked as a Senior Research Scientist at CEDAR (State University of New York at Buffalo) and as a postdoctoral fellow at the Université du Québec à Montréal, contributing to several industrial research projects.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt;\"\u003eHe earned a Master’s degree in Mathematics and a Ph.D. in Computer Science from Grenoble University. He serves on the Editorial Board of \u003cem\u003ePattern Recognition\u003c\/em\u003e (Elsevier), is a Senior Member of IEEE, and is a founding member of the Algerian Academy of Sciences and Technologies (AAST).\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e25 August 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032249531\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e767\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":46921117139084,"sku":"9783032249531","price":62.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032249531.jpg?v=1780605020","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032249531","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}