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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

Dieses Buch bietet einen elementaren und anschaulichen Einstieg in die Programmierung für mathematische Zwecke sowie die damit verbundenen Kompetenzen. Es konzentriert sich auf wenige grundlegende Konzepte und verwendet zur Umsetzung die leicht zugängliche Programmiersprache Python; dadurch wird ohne großen technischen Ballast eine große Vielfalt mathematischer Anwendungen erschlossen – von der Zahlentheorie und klassischen Numerik über Optimierung, Differentialgleichungen und Simulationen hin zur 3D-Computergrafik, Clusteranalyse und Computeralgebra. Die einzelnen Kapitel beinhalten Übungsaufgaben, die von und einfachen technischen Übungen bis zu Miniprojekten reichen.
Das Buch richtet sich insbesondere an Lehramtsstudierende und bietet bei Bedarf die Möglichkeit zum Einsatz in der Schule: Viele der vorgestellten Themen und Projekte können bereits ab der späten Sekundarstufe I und der Sekundarstufe II bearbeitet werden.
Für die vorliegende 2. Auflage wurden etliche Überarbeitungen vorgenommen: Die Inhalte wurden aktualisiert, teilweise gestrafft, einige Themen wurden ausgebaut bzw. neu aufgenommen – etwa Computational Thinking und KI. Die Beispieldateien und Lösungen der Aufgaben werden nun online als Jupyter-Notebooks bereitgestellt, so dass direkt damit gearbeitet werden kann. Neu sind außerdem kurze Fragen, die zum Innehalten, Reflektieren sowie ggf. eigenen Ausprobieren einladen.

Mathematik aktiv erleben durch Programmieren in Python

Mathematische Algorithmen im Unterricht

Child Safeguarding in the Digital Age

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

The MIT Press Essential Knowledge series

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.

Auditing AI

De Gruyter Studium

Nach einer Einführung zum Aufbau von Rechnersystemen und zur Darstellung von Informationen folgt ein Einstieg in die Programmierung mit der Sprache Python. Dabei werden grundsätzliche Prinzipien von Programmiersprachen erläutert, darunter Schleifen, Rekursion, imperative, funktionale und objektorientierte Programmierkonzepte. Einige konkrete Projekte werden in Python realisiert, so etwa zur Datenbeschaffung im Internet und deren Aufbereitung oder zum Umgang mit diversen Sensoren und zur Steuerung externer Geräte mit dem Raspberry-Pi. Dem Objektorientierten Programmieren und insbesondere der Programmiersprache Java ist ein eigenes Kapitel gewidmet. Diese Sprache und ihre Infrastruktur unterstützen besonders die professionelle Entwicklung großer Projekte. Auch die neuesten Konzepte von Java (Lambdas, Ströme und Funktionale) werden anschaulich erläutert. Das letzte Kapitel behandelt klassische Algorithmen und Datenstrukturen: Such- und Sortieralgorithmen, Listen, Bäume, Graphen, Maps, und diverse andere Datentypen zum effi zienten Speichern, Wiederauffi nden und Transformieren von Daten. Diese werden mit ihren Vor- und Nachteilen und anhand von Java-Programmen dargestellt. Der zweite Band ist technischen Themen gewidmet - insbesondere der Rechnerarchitektur, Betriebssystemen, Rechnernetzen und speziell dem Internet. Der dritte und letzte Band der Buchreihe Informatik ist der Theoretischen Informatik gewidmet. Das Buch richtet sich an alle Einsteiger, die sich ernsthaft mit Informatik beschäftigen wollen, sei es zum Selbststudium oder zur Begleitung von Vorlesungen. In den folgenden Bänden dieses Buches werden die Themen, Rechnerarchitektur, Betriebssysteme, Rechnernetze, Internet, Compilerbau und Theoretische Informatik vertieft. Prof. Dr. Heinz-Peter Gumm ist Professor für Theoretische Informatik in Marburg. Nach dem Studium in Darmstadt und Winnipeg (Kanada) von 1970 bis 1975 und der Habilitation 1981 folgten Professuren in Hawaii, Kalifornien und New York. Seine Forschungsgebiete sind Formale Methoden, Allgemeine Algebren und Coalgebren. Prof. Dr. Manfred Sommer ist emeritierter Professor für Praktische Informatik in Marburg. Nach dem Studium in Göttingen und München von 1964 bis 1969, war er Assistent am ersten Informatik-Institut in Deutschland an der TU München. Es folgten zehn Jahre bei Siemens in München und von 1984 bis 2014 war er Informatik-Professor in Marburg.

Programmierung, Algorithmen und Datenstrukturen

Informatik

How AI can positively impact so many aspects of our daily lives, from health and wellness to work, education, and home life. Artificial intelligence (AI) is a powerful general-purpose technology that is reshaping the modern economy, but misperceptions about AI stand in the way of harnessing it for the betterment of humanity. In Thrive, Ravi Bapna and Anindya Ghose counter the backlash by showcasing how AI is positively influencing the aspects of our daily lives that we care about most: our health and wellness, relationships, education, the workplace, and domestic life. In the process the authors help explain the underlying technology and give people the agency they need to shape the debate around how we should regulate AI to maximize its benefits and minimize its risks.

Maximizing Well-Being in the Age of AI

Thrive

Die drei Teile der "Informatik - Einführung" decken alle Inhalte des kompletten Informatik-Bachelorstudiums ab. Die einzelnen Teile sind auf die Inhalte der einzelnen Semester abgestimmt. Jeder Teil liefert unabhängig die Grundlagen für die jeweiligen Themen. Durch seinen didaktischen Aufbau mit Aufgaben ist das Buch auf die Bachelorstudierenden abgestimmt. Durch seine anschauliche Darstellund sowie Beispiele aus der Praxis holt es die Einsteiger bei deren Wissensstand ab. Der erste Band der Informatik erklärt die grundlegenden Konzepte: Programmierung, Algorithmen und Datenstrukturen. Der dritte und letzte Band der Buchreihe Informatik ist der Theoretischen Informatik gewidmet. Das Buch richtet sich an alle Einsteiger, die sich ernsthaft mit Informatik beschäftigen wollen, sei es zum Selbststudium oder zur Begleitung von Vorlesungen. In den folgenden Bänden dieses Buches werden die Themen, Rechnerarchitektur, Betriebssysteme, Rechnernetze, Internet, Compilerbau und Theoretische Informatik vertieft. Prof. Dr. Heinz-Peter Gumm ist Professor für Theoretische Informatik in Marburg. Nach dem Studium in Darmstadt und Winnipeg (Kanada) von 1970 bis 1975 und der Habilitation 1981 folgten Professuren in Hawaii, Kalifornien und New York. Seine Forschungsgebiete sind Formale Methoden, Allgemeine Algebren und Coalgebren. Prof. Dr. Manfred Sommer ist emeritierter Professor für Praktische Informatik in Marburg. Nach dem Studium in Göttingen und München von 1964 bis 1969, war er Assistent am ersten Informatik-Institut in Deutschland an der TU München. Es folgten zehn Jahre bei Siemens in München und von 1984 bis 2014 war er Informatik-Professor in Marburg.

Rechnerarchitektur, Betriebssysteme, Rechnernetze und Künstliche Intelligenz

A dramatic new future of work in which managers assemble exactly the expertise they need—within minutes. A USA Today Bestseller Thinkers50 Talent Award Shortlist The Next Big Idea Club: “October 2025 Must-Read Books” 2026 PROSE Award Winner: Business, Finance, and Management 2026 Axiom Business Book Awards: Gold Medal, Business Disruption/Reinvention Shortlisted for the 2026 getAbstract International Book Award, Business Impact category "If you’ve ever wished you could assemble your version of The Avengers at work, this book will help you make it happen.” —Adam Grant, #1 New York Times-bestselling author of Think Again and host of the podcast Re:Thinking Gone are the days of static organizational charts and staffing based on the manager’s rolodex and intuition. Now you can recruit any expertise you need from a global online network within minutes: an on-demand, on-the-spot expert at the exact moment that you need their help. You can right-size their involvement, too; some of those experts give a second opinion or a moment of brainstorming, whereas others join as full-fledged team members for a sustained collaborative effort. This is the future promised by flash teams, a model that The New York Times has already praised for its “revolutionary potential”: a world where experts are available anytime and everywhere, where remote work has become a norm, and where AI is in the loop to guide team decisions. In Flash Teams, award-winning management scholar Melissa Valentine and computer scientist Michael Bernstein chart the opportunities of flash teams and navigate the challenges that teams and managers will face. They distill lessons from their own work assembling and managing flash teams on demand that every manager can learn from so they can successfully use flash teams in their own organizations. Drawing on original research and industry examples, this book will help readers to: <ul><li>Recognize and source needed expertise within minutes</li><li>Anticipate and plan team designs with AI</li><li>Redesign team structures on the fly as projects evolve</li></ul> Industries are already being transformed by this new approach to teaming. Flash Teams arms leaders, managers, and entrepreneurs with the tools they need to accomplish their goals with confidence, speed, and agility.

Leading the Future of AI-Enhanced, On-Demand Work

Flash Teams

'This is the bible to living a healthier life' DAVINA MCCALL | 'No-one explains cutting edge technology more clearly than Lara' LORRAINE KELLY | 'Absolutely brilliant' JEREMY VINE | 'Extraordinarily helpful' DAMIAN HUGHES | 'Brilliantly written ... separates the nonsense from the evidence' RUSSELL FOSTERWhat if we could stop cancer in its tracks, prevent cardiovascular disease, lose weight at will and reverse the effects of ageing? With new developments in artificial intelligence, gene editing and robotics, it might all be possible sooner than you think.In Hacking Humanity, technology journalist Lara Lewington takes us to the cutting edge of scientific research to demystify how new innovations are transforming medicine for the better. Drawing us into the science behind the world's healthiest people from Silicon Valley to the Blue Zones while experimenting with the new technology already available, she examines the real challenges ahead and how we can overcome them. From new miracle drugs to life-saving robotic assistants and diagnostic tools that help us avoid disease altogether, today's most exciting innovations are paving the way for tomorrow's healthcare revolution, extending not only our lifespans but also our healthspans - the amount of time we spend living in good health.With expert insights and clear explanations, Hacking Humanity reveals how we can harness the power of technology to improve our lives now and transform our healthcare system for a better future, unlocking the power to reshape our genetic destinies and live longer and healthier lives.'What Lara does so beautifully in Hacking Humanity is cut through the hype and show how innovation can be human, hopeful and practical, helping us prevent illness, not just treat it' DR AMIR KHAN

The Innovations Helping Us Live Healthier Lives for Longer

Hacking Humanity

Toward a deeper understanding of minds, whether natural or artificial, and of how they are studied and built across AI, AGI, and cognitive science. Minds are functional entities that enable intelligent behavior. In On Minds, Paul Rosenbloom offers reflections and perspectives on the study of minds arising from a multi-decade career focused largely on cognitive architectures—hypotheses concerning the fixed structures and processes that yield a mind, whether natural or artificial. Cognitive architectures must be integrative in accommodating the full range of capabilities required of a mind. They must also be general, in applying across many, if not all, problems and domains. This breadth of investigation, across a sequence of three such architectures—XAPS, Soar, and Sigma—and over many years, has inspired a set of reflections on both cognitive architectures and three core disciplines within which such work occurs—artificial intelligence (AI), artificial general intelligence (AGI), and cognitive science. The book’s reflections are of three types. The first explores four concepts critical to understanding minds—architectures, symbols, intelligence, and theories—to provide new perspectives and insights on them. The second rethinks the structure of the space of technologies that underlies work on minds, yielding multidimensional maps that clarify their similarities and differences plus detailed insights into the tradeoffs that exist across both individual technologies and integrated systems—whether symbolic, neural, or hybrid—built from them. The third traces the author’s decades of work on architectures—including cognitive architectures, the Common Model of Cognition, and the nature and structure of computing as a great scientific domain—interwoven with personal reflections and anecdotes.

Reflections of a Cognitive Architect

On Minds

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

Causal Inference with Bayesian Networks

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