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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

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

How do we live alongside machines that are sometimes smarter than us? We are entering a new era. AI systems can already outperform humans in some domains while failing spectacularly in others. They can act as tutors, collaborators, researchers, strategists, and creators...yet they can also produce falsehoods, generate convincing nonsense, and fail unpredictably. The challenge is no longer simply adopting new technology. It is learning how to think, work, and live in a world where machines are sometimes smarter than us. In Co-Existence, Ethan Mollick—a Wharton professor and author of the widely read One Useful Thing newsletter—argues that the future of AI will not be defined by replacement, but by interaction. Drawing on years of research, experimentation, and frontline experience with rapidly evolving AI systems, Mollick shows readers how to: • work productively with AI while retaining judgment and autonomy; • distinguish where human insight matters most; • avoid becoming overly dependent on systems designed to persuade and imitate; • adapt creatively as AI reshapes education, business, and everyday life; and • build a durable human advantage in a world of intelligent machines. Clear-eyed, practical, and written with warmth and humor, Co-Existence is your guide to navigating the next phase of the AI revolution.

The Next Phase of AI

Co-Existence

Texts in Computer Science

Computational geometry plays a vital role in fields ranging from algorithms, data structures, robotics, and computer graphics to geographic information systems and AI. Providing a comprehensive toolkit, this core textbook constructs a strong bridge between rigorous geometric theory and practical, real-world implementations—making complex concepts accessible and engaging. Uniquely, the volume presents a modern approach to computational geometry through the lens of Python programming. Emphasizing clarity, structure, and visualization, the book introduces core geometric structures and algorithms, supported by detailed illustrations and interactive examples. With its project-based learning orientation and emphasis on conceptual understanding, it can serve as both a textbook and a reference guide for those exploring the computational side of geometry.Topics and features:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; All geometric algorithms implemented in native Python·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 400+ illustrations and visualizations·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Includes project-based exercises for students·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Coverage: Core Structures and Algorithms, Geometric Objects in Python, Algorithms for Geometric Objects, Convex Hull Algorithms in 2D and 3D, Polygon Triangulation Methods, Delaunay Triangulation, Voronoi Diagrams, Visualization Techniques, Algorithms for Space Exploration, Quadtrees, Robot Motion Planning, AI in Computational Geometry·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Suitable for advanced undergraduate and graduate courses·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GitHub repository with all source codeIdeal for students of computer science, the textbook/reference will have real utility as well for those in mathematics and engineering.&nbsp; Researchers and practitioners working in computational geometry, algorithm design, data visualization, or related disciplines will find the work an indispensable resource and guide.Adis Alihodžić
 is a Full Professor at the Department of Mathematics and Computer Science, Faculty of Natural Sciences and Mathematics, University of Sarajevo.

Theory and Python Implementations

Exploring Computational Geometry

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

A timely, practical guide to AI—its strengths, weaknesses, and real-world applications—for business professionals and policymakers. Artificial intelligence, or AI, can recognize a pattern from any set of data it is given, which is what makes it such an extraordinarily powerful tool. But because not all patterns are authentic or reliable, AI's pattern-finding superpower can lead to spurious patterns—and to disastrous results for business and government entities that rely on them. Hence the conundrum at the heart of AI: its greatest strength can also be its greatest weakness. Targeting the businessperson who needs to know how to use AI profitably and responsibly, Caleb Briggs and Rex Briggs offer in this book a foundational understanding of AI that is easy to grasp yet thorough enough to be used effectively.

Harnessing the Power of AI for Your Organization—Profitably and Safely

The AI Conundrum

AN INSTANT NATIONAL BESTSELLER “Evenhanded and insightful.”&nbsp; —Publisher’s Weekly (starred review) “An accessible, often fascinating primer.... Essential, illuminating reading.” —Kirkus From the author of the New York Times bestseller The Digital Doctor comes an engaging, clear-eyed, and ultimately hopeful examination of healthcare’s efforts to embrace generative artificial intelligence. In A Giant Leap, physician and thought leader Robert Wachter navigates between hype and skepticism to make a compelling case for AI’s power to transform healthcare. He argues that, in a system buckling under the weight of bureaucratic pressures, soaring costs, and clinician burnout, AI doesn’t need to be perfect—it only needs to be better. Drawing on extensive research and more than 100 interviews with pioneers across medicine, technology, policy, and business, Wachter shows how AI is already entering hospitals and clinics to draft notes, field patient questions, recommend treatments, interpret images, and guide surgeries. He unflinchingly confronts risks like hallucinations, biases, and misinformation, while revealing how AI can now match, and sometimes surpass, physicians in areas ranging from diagnosis to empathy. But this isn’t simply a technology story. It’s about the human choices that will determine whether AI becomes healthcare’s salvation or another source of harm and frustration.&nbsp; Blending clinical insight, vivid storytelling, and journalistic precision, A Giant Leap offers an indispensable roadmap for healthcare leaders, clinicians, and patients. It&nbsp;is a vibrant and timely account of how AI is changing what it means to care—and be cared for—in this age of astonishing technology.

How AI Is Transforming Healthcare and What That Means for Our Future

A Giant Leap

How to Make Sense of a Complex Future

The AI Paradox

From Conceptual Algorithms to Real-World Applications

Methods of Nonsmooth Optimization in Stochastic Programming

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.

A Practical Guide for Nonprogrammers

Automate Excel with Python

Build a more secure network with PF. The OpenBSD packet filter, PF, is central to the OpenBSD and FreeBSD network toolbox. With more services placing high demands on bandwidth and an increasingly hostile Internet environment, no sysadmin can afford to be without PF expertise. The fourth edition of The Book of PF covers the most up-to-date developments in PF, including new content on IPv6, dual stack configurations, the “queues and priorities” traffic-shaping system, NAT and redirection, wireless networking, spam fighting, failover provisioning, logging, and more.&nbsp; You’ll also learn how to: <ul><li>Create rulesets for all kinds of network traffic, IPv4 and IPv6 both, whether crossing a simple LAN, hiding behind NAT, traversing DMZs, or spanning bridges or wider networks</li><li>Set up wireless networks with access points, and lock them down using authpf and special access restrictions</li><li>Maximize flexibility and service availability via CARP, relayd, and redirection</li><li>Build adaptive firewalls to proactively defend against attackers and spammers</li><li>Harness OpenBSD’s latest traffic-shaping system to keep your network responsive, or use ALTQ and Dummynet configurations on FreeBSD to full effect</li><li>Stay in control of your traffic with monitoring and visualization tools (including NetFlow)</li></ul> The Book of PF is the essential guide to building a secure network with PF. With a little effort and this book, you’ll be well prepared to unlock PF’s full potential. Covers OpenBSD 7.x, FreeBSD 14.x, and NetBSD 10.x

A No-Nonsense Guide to the OpenBSD Firewall

The Book of PF, 4th Edition

This book offers an in-depth exploration of artificial intelligence (AI) in human resource development (HRD). AI's transformative impact on work, learning, and organizational systems places HRD at the core of this challenge. Addressing the challenge, the book is structured into four parts. The initial section scrutinizes and frames the foundations for this challenge. The subsequent part examines the governance and ethical considerations, while the third part focuses on the design of AI-enabled HRD systems and processes. Part Four examined the human dimension, including workforce development and leadership.&nbsp;&nbsp;Overall, the chapters offer a comprehensive perspective of the critical issues around AI and HRD, linking individuals, organizations, and society. It will advance scholarship in this emerging area of research and address debates within the field among HRD scholars and scholar-practitioners about the role of AI in HRD.Jessica Li 
 is a Professor of Human Resource Development in the Department of Education Policy, Organization &amp; Leadership, College of Education at the University of Illinois, Urbana-Champaign, USA.
&nbsp;

 Amin Alizadeh&nbsp;
 is an Assistant Professor of Human Capital Development in the School of Leadership at the University of Southern Mississippi, USA.
&nbsp;

 Khalil Dirani&nbsp;
 is a Professor of Human Resource Department and Department Head at the Department of Educational Administration and Human Resource Development at Texas A&amp;M University, USA.

Transforming and Redesigning the Process

Artificial Intelligence in Human Resource Development

Dance with the Dragon “A must-read for any reverse engineer.” ­—Max “Libra” Kersten Developed through more than a decade of research within the NSA, Ghidra was created to address some of the most challenging reverse engineering problems faced by the US government. With its open source release, this formerly restricted tool suite is now freely available to cybersecurity practitioners, researchers, and students worldwide. The Ghidra Book provides a clear, practical guide to understanding and using this powerful platform. In addition to introducing core reverse engineering techniques for software and malware analysis, the book explains Ghidra’s key components, features, and support for extensibility and collaborative analysis. Beginning with the fundamentals and progressing to more advanced workflows, you’ll learn how to use Ghidra effectively and adapt it to new challenges. You’ll learn how to: <ul><li>Navigate and interpret a disassembly</li><li>Use Ghidra’s built-in decompiler to expedite analysis</li><li>Analyze unfamiliar and obfuscated binaries</li><li>Extend Ghidra to recognize new data types</li><li>Build custom analyzers, loaders, and processor support modules</li><li>Script Ghidra tasks to automate analysis tasks</li><li>Set up a collaborative reverse engineering environment</li></ul> This fully revised second edition reflects the modern Ghidra platform as it is used in practice, with updated tooling, improved workflows, and hands-on real-world examples. Designed for beginners and experienced users alike, The Ghidra Book prepares you to tackle real reverse engineering problems with confidence. New to this edition: <ul><li>Behavioral analysis with BSim</li><li>Full Python 3 support via PyGhidra</li><li>Enhanced debugging and graphing tools</li><li>Modern container-based deployment</li></ul>

The Definitive Guide

The Ghidra Book, 2nd Edition

Causal Inference with Bayesian Networks

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