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Bayes, AI, and Deep Learning
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Bayes, AI, and Deep Learning : Foundations of Data Science

Book Details

Format Paperback / Softback
ISBN-10 1032467983
ISBN-13 9781032467986
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Dec 15th, 2026
Print length 818 Pages
Weight 453 grams
Ksh 12,050.00
Not Yet Published 0 in stock

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This book is based on a course taught at Chicago Booth to MBA and PhD students on Bayesian Statistics, Artificial Intelligence, and Deep Learning. It pulls together these three related topics into a unified framework of study, building up from the basics of probability and Bayesian inference, through AI and deep learning.
From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook. Key Features:Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breakingBridges classical statistics and deep learning, connecting linear regression to transformersCovers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMsEmphasizes uncertainty quantification—building systems that know what they don't knowPractical applications across finance, healthcare, operations, and autonomous systemsEmerging topics: AI agents, RLHF, and retrieval-augmented generationWritten for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

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