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Fattah Y: Causal Inference with Bayesian
Networks

Causal Inference with Bayesian Networks

Build Bayesian Networks and Causal Inference Models with R and Python

From Conceptual Algorithms to Real-World Applications

Methods of Nonsmooth Optimization in Stochastic Programming

Mental health technology companies have become some of social media's biggest advertisers and a prime source of endorsement deals with influencers, many of whom are also professional therapists. In Mood Disorder, Holly Avella explores how digital therapeutic technologies, including popular mood-tracking apps and algorithmic social media, work to construct branded psychiatric identities and calibrate the mood of contemporary mental health discourse and practice. Through more than five years of mixed-method, qualitative research, Avella investigates what happens when media platforms are given diagnostic authority. Moving beyond critiques of mental health media effects and misinformation, Avella honors the coping functions of therapy tech while probing the moody histories of diagnostic mental health, from hysteria photos to the aestheticized meds delivered to your door. Ultimately, this book asks readers to consider if more equitable and just forms of care are possible in these spaces.

How Social Media and Therapy Tech Are Shaping Mental Health

Mood Disorder

International Series in Operations Research & Management Science

This book provides a comprehensive exploration of permutation flowshop group scheduling, focusing on sequence-dependent setup times (SDST) to minimize makespan and total completion time. Grounded in Group Technology philosophy, it bridges the gap between theoretical rigor and industrial practice, synthesizing over fifty years of research.The text covers foundational scheduling concepts, computational complexity, and the formal classification of the Flowshop Group Scheduling Problem. It details exact and heuristic solution methodologies, including Mixed-Integer Linear Programming, sophisticated lower-bounding techniques, constructive heuristics, and metaheuristics like Tabu Search and Genetic Algorithms. Innovations include using inserted machine idle times to minimize makespan under SDST constraints.Unique features include consideration of carryover setups, which are critical in electronics manufacturing, such as Printed Circuit Board assembly. Applications extend beyond manufacturing to service sectors like healthcare and cloud processing. Concluding with the "AI Revolution," the book recommends Deep Reinforcement Learning for real-time decision-making.This book is designed as a definitive reference for students, researchers, and software developers, as well as professionals and consultants seeking a higher-level understanding of shop floor control and intelligent systems innovation.

Sequence Dependent Setup Times

Flowshop Group Scheduling

How AI is transforming pricing decisions, and what opportunities, challenges, and risks these transformations bring. Pricing in the Age of AI explores the evolution of pricing decisions, with a particular emphasis on recent advancements. In the age of big data and AI, pricing strategies have undergone a dramatic transformation. For instance, in e-commerce, prices can now be adjusted every few seconds, tailored to individual users or personas, and optimized with precision. This transformation has the potential to disrupt pricing decisions on an unprecedented scale. Author Maxime Cohen begins by tracing the history and importance of pricing decisions. He focuses on the transformative changes of the past decade, highlighting how big data and AI have upended traditional pricing strategies. He also examines the psychological and behavioral tactics employed to influence consumer behavior through pricing, as well as how recent advancements in AI—and generative AI—are revolutionizing pricing practices, enabling managers to automate, optimize, and personalize decisions at scale. Finally, he discusses the challenges and risks associated with these innovations, including ethical concerns such as anticompetitive practices, consumer protection, and discrimination issues.

How AI is transforming pricing decisions, and what opportunities, challenges, and risks these transformations bring. Pricing in the Age of AI explores the evolution of pricing decisions, with a particular emphasis on recent advancements. In the age of big data and AI, pricing strategies have undergone a dramatic transformation. For instance, in e-commerce, prices can now be adjusted every few seconds, tailored to individual users or personas, and optimized with precision. This transformation has the potential to disrupt pricing decisions on an unprecedented scale. Author Maxime Cohen begins by tracing the history and importance of pricing decisions. He focuses on the transformative changes of the past decade, highlighting how big data and AI have upended traditional pricing strategies. He also examines the psychological and behavioral tactics employed to influence consumer behavior through pricing, as well as how recent advancements in AI—and generative AI—are revolutionizing pricing practices, enabling managers to automate, optimize, and personalize decisions at scale. Finally, he discusses the challenges and risks associated with these innovations, including ethical concerns such as anticompetitive practices, consumer protection, and discrimination issues.

Pricing in the Age of AI

Today our world is under the watchful and tireless eye of computer vision, with cam­eras and monitors tracing our every move. Furthermore, generative AI is now able to render a synthetic world indistinguishable from reality for us to explore. Trevor Paglen goes in search of the ways and means of understanding this new visual universe. Instead of asking what these technologies "say" about the world, he teaches us to ask what they "do" and where such images come from. Exploring the esoteric worlds of psyops, UFO imagery, magicians, and public relation gurus, Paglen shows that this appar­ently alien realm is more human, but much stranger, than we imagine.

Images After AI

How to See Like a Machine

Chapman & Hall/CRC Texts in Statistical Science

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:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written 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.

Foundations of Data Science

Bayes, AI, and Deep Learning

In an age where social media platforms structure the very conditions of visibility, this book investigates how LGBTQ lives are made, managed, and sometimes erased by algorithmic infrastructures.Bringing together voices from across the Global South and diasporic communities, this interdisciplinary volume examines how queer and trans individuals navigate digital platforms as spaces of expression, surveillance, resistance, and survival. Spanning geographies from Bangladesh to China, and Pakistan to the Philippines, contributors explore the affective, political, and economic entanglements of platformed queer life. The chapters interrogate how algorithms sort content, how visibility becomes conditional, and how queer resistance emerges not only through representation, but through subversion, invisibility, memory work, and infrastructural reappropriation. Foregrounding intersectionality, decolonial critique, and transnational feminist and queer theory, this collection reframes digital life not as a neutral terrain, but as one where queerness is contested, commodified, and continuously reimagined. This book is both an urgent critique of platform capitalism and a testament to queer world-building amid precarity and constraint.A timely exploration of the algorithmic, economic, and socio-technical undercurrents that shape queer lives online, this book will be of interest to students, researchers, and academics working in the areas of queer media, global digital culture, critical platform studies, and media and activism.

LGBTQ Realities and Resistance under Platform Capitalism

Queer Digital Lives

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it. “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.” —Scott Galloway, NYU Stern Professor of Marketing; bestselling author of The Four “An antidote to today’s relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.” —Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better The greatest tool is the hardest to use. Machine learning is the world's most important general-purpose technology—but it's notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What's missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects, aka predictive AI projects, from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals, and it establishes a sorely needed strategic framework for data professionals. Beyond detailing the practice, this book also upskills business professionals—painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative—getting them right paves the way for machine learning's value-driven deployment. A note from the author: The buzzword AI can mean many things, but this book is about the most vital use cases of machine learning,&nbsp;those designed to improve large-scale business operations—aka predictive AI or predictive analytics.

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.

Mastering the Rare Art of Machine Learning Deployment

The AI Playbook, with a new preface by the author

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

AI can write the code. You still need to know what it does. You’re already good at Excel. But you’re tired of the copy/paste, the helper columns, and the brittle formulas that break when someone adds a row. You want automation, and whether you write the code yourself or let AI generate it, you need to understand what’s actually running your business processes. Automate Excel with Python teaches you to build real workflows, step-by-step. You’ll read messy workbooks into pandas dataframes, filter and reshape data without helper columns, merge sources without silent VLOOKUP failures, and export polished results with formatting intact. A capstone chapter ties it together: import a multi-tab workbook, generate exception reports, and email the results—all from one script you run with a single click. You’ll learn how to: <ul><li>Read existing Excel files you already use into Python, even the messy ones</li><li>Replace daily copy/paste routines with reusable scripts</li><li>Merge and match data across sources with auditable results</li><li>Handle dates, times, and the edge cases that break formulas</li><li>Export from Python to Excel with column widths, number formats, and frozen panes intact</li><li>Build workflows that run daily without babysitting</li></ul> AI can write the code. This book makes sure you’re the one in control of it.

This practical guide will help spreadsheet pros save time and boost productivity using beginner Python tools – without having to become a programmer. Automate Excel with Python bridges the gap between Excel expertise and Python programming without forcing you to start from scratch. Unlike other programming guides, it’s specifically designed for Excel users who need more power and automation, not for professional programmers. This book focuses on enhancing your existing Excel skills rather than replacing them. You’ll learn how Python’s dataframes work like virtual spreadsheets, extending what you already know while automating repetitive tasks. You’ll be able to import and export data seamlessly between Excel and Python to integrate coding into your current workflow. Concise, practical examples throughout will get you up and running quickly. Each topic addresses real problems Excel users face, with solutions you can implement immediately. The structure is deliberately flexible. Chapters progress logically but stand independently, so you can jump to exactly what you need without reading everything that came before. This makes it perfect for solving urgent problems or learning at your own pace. You’ll apply the techniques you learn to automate reports, clean messy data, perform complex calculations, and handle large datasets that would overwhelm Excel alone—all while leveraging your existing spreadsheet knowledge.

A Practical Guide for Nonprogrammers

Automate Excel with Python

How tech companies, journalists, and policymakers can prevent AI decision-making from going wrong. Our lives are increasingly governed by automated systems influencing everything from medical care to policing to employment opportunities, but researchers and investigative journalists have proven that AI systems regularly get things wrong. Auditing AI is a first-of-its-kind exploration of why and how to audit artificial intelligence systems. It offers a simple roadmap for using AI audits to make product and policy changes that benefit companies and the public alike. The book aims to convince readers that AI systems should be subject to robust audits to protect all of us from the dangers of these systems. Readers will come away with an understanding of what an AI audit is, why AI audits are important, key components of an audit that follows best practices, how to interpret an audit, and the available choices to act on an audit’s results. The book is organized around canonical examples: from AI-powered drones mistakenly targeting civilians in conflict areas to false arrests triggered by facial recognition systems that misidentified people with dark skin tones to HR hiring software that prefers men. It explains these definitive cases of AI decision-making gone wrong and then highlights specific audits that have led to concrete changes in government policy and corporate practice. The Marquand House Collective: Marc Aidinoff, Lena Armstrong, Esha Bhandari, Ellery Roberts Biddle, Motahhare Eslami, Karrie Karahalios, Nate Matias, Danaé Metaxa, Alondra Nelson, Christian Sandvig, and Kristen Vaccaro.

How tech companies, journalists, and policymakers can prevent AI decision-making from going wrong.

Auditing AI

From the New York Times bestselling authors of Abundance, Bold, and The Future Is Faster Than You Think comes a bold exploration of what it means to stay human in a world where technology has granted us godlike power. 'Timely and important' Eric Schmidt 'A manual for optimizing our destiny' Ray Kurzweil 'An extraordinary blueprint for hope' Mo Gawdat In 1968, Stewart Brand declared: 'We are as gods - and we might as well get good at it.' Half a century later, that prophecy has come true. We can rewrite genes, edit embryos, build artificial minds, extend life, and terraform worlds. The old miracles - omniscience, omnipresence, even resurrection - are becoming standard operating procedure. But the real question isn't whether humanity can play god. It's whether we can do it wisely. In this book, the bestselling authors of Abundance and Bold return with a sweeping exploration of our species' next great transformation. Blending hard science with vivid storytelling, Peter Diamandis and Steven Kotler chart humanity's ascent from scarcity to superabundance - and the psychological, ethical, and existential challenges that come with it. Across breakthroughs in AI, robotics, genetics, longevity, and consciousness research, they reveal a paradox at the heart of progress: as our external power expands, our inner resilience must evolve to match. Abundance without meaning leads to collapse. Intelligence without wisdom leads to extinction. To thrive in a world of everything, everywhere, all the time, we must learn to wield our godlike powers with humility, creativity, and flow. Equal parts warning and invitation, We Are As Gods is a map for flourishing in the exponential century. Because the future won't be built by those who fear what's coming, but by those who know how to turn chaos into creation. Abundance is here. Are you ready?

A Survival Guide for the Age of Abundance

We Are As Gods

Causal Inference with Bayesian Networks

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