The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.

    Imagine that you face three doors, behind one of which is a prize. You choose one but do not open it. The host--call him Monty Hall--opens a different door, alwayschoosing one he knows to be empty. Left with two doors, will you do better by sticking with your first choice, or by switching to the other remaining door? In this light-hearted yet ultimately serious book, Jason Rosenhouse explores the history of this fascinating puzzle. Using a minimum ofmathematics (and none at all for much of the book), he shows how the problem has fascinated philosophers, psychologists, and many others, and examines the many variations that have appeared over the years. As Rosenhouse demonstrates, the Monty Hall Problem illuminates fundamental mathematical issuesand has abiding philosophical implications. Perhaps most important, he writes, the problem opens a window on our cognitive difficulties in reasoning about uncertainty.

    The book has been designed to offer maximal accessibility to the widest range of students (not only those majoring in philosophy) and assumes no formal training in elementary symbolic logic. It offers a comprehensive course covering all basic definitions of induction and probability, and considers such topics as decision theory, Bayesianism, frequency ideas, and the philosophical problem of induction. The key features of the book are: * A lively and vigorous prose style* Lucid and systematic organization and presentation of the ideas* Many practical applications* A rich supply of exercises drawing on examples from such fields as psychology, ecology, economics, bioethics, engineering, and political science* Numerous brief historical accounts of how fundamental ideas of probability and induction developed.* A full bibliography of further reading Although designed primarily for courses in philosophy, the book could certainly be read and enjoyed by those in the social sciences (particularly psychology, economics, political science and sociology) or medical sciences such as epidemiology seeking a reader-friendly account of the basic ideas of probability and induction. Ian Hacking is University Professor, University of Toronto. He is Fellow of the Royal Society of Canada, Fellow of the British Academy, and Fellow of the American Academy of Arts and Sciences. he is author of many books including five previous books with Cambridge (The Logic of Statistical Inference, Why Does Language Matter to Philosophy?, The Emergence of Probability, Representing and Intervening, and The Taming of Chance).

    Covering Sid's early years in South Africa and life as a ladies' hairdresser, his obsession with gambling and women, his questioning by Scotland Yard in a murder case, Hancock's Half Hour and the Carry On films, and Sid's death on stage at the age of 63, Cliff Goodwin reveals the amazing truth behing the legend.

    More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it.The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as "computers") who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT--methods, machines, computing education, software engineering, computational science, and design--and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.

     Buddhism is an agnostic religion. It neither acknowledges the existence of a god nor denies it. It simply teaches that we must live by a moral code because it is our nature to do so, regardless of whether a god exists or not. To choose good in the hopes of reward, while avoiding evil out of fear of punishment, is not true goodness. It is sheer hypocrisy — a selfish desire to do something in return for our own benefit. To understand the Four Noble Truths and the Eightfold Path, we first have to understand the word “dukkha.” This is often mistranslated into English as “suffering,” giving people the idea that Buddhism is a pessimistic religion. Nothing can possibly be further from the truth. While dukkha can certainly be understood to mean “suffering,” it would be more accurate to translate this word as “anxiety,” “stress,” or “dissatisfaction.” This book endeavors to explain the Buddha’s perspective on dukkha, and how one can live in spite of it, even striving to move beyond it. If you’re ready to learn more about dukkha and the path to liberation, let’s get started! Here Is A Preview Of What You'll Learn... About Buddhist Diversity Understanding Dukkha The Four Noble Truths The Eightfold Path Panna – Wisdom Śila – Ethical Conduct Samādhi – Concentration Nibbāna – Blown Out Much, much more! Download your copy today! Tags: eight-fold path, nirvana, the four noble truths and the eightfold path, four noble truths and eightfold path, buddhism, buddhist, theraveda buddhism, Eightfold Path, four noble truths, nibbana, eightfold path of buddhism, the eightfold path, noble eightfold path, eight fold path

    The first chapter describes basic features of the program and shows how to use it in simple arithmetic operations with scalars. The next two chapters focus on the topic of arrays (the basis of MATLAB), while the remaining text covers a wide range of other applications. Computer screens, tutorials, samples, and homework questions in math, science, and engineering, provide the student with the practical hands-on experience needed for total proficiency.

    Turing’s openness about his homosexuality at a time when it was an imprisonable offense ultimately led to his untimely lo death at the age of only forty-one. In Alan Turing: Unlocking the Enigma, David Boyle reveals the mysteries behind the man and his remarkable career. Aged just 22, Turing was elected a fellow at King's College, Cambridge on the strength of a dissertation in which he proved the central limit theorem. By the age of 33, he had been awarded the OBE by King George VI for his wartime services: Turing was instrumental in cracking the Nazi Enigma machines at the top secret code breaking establishment at Bletchley Park during the Second World War.But his achievements were to be tragically overshadowed by the paranoia of the post-War years. Hounded for his supposedly subversive views and for his sexuality, Turing was prosecuted in 1952, and forced to accept the humiliation of hormone treatment to avoid a prison sentence. Just two years later, at the age of 41 he was dead. The verdict: cyanide poisoning.Was Turing’s death accidental as his mother always claimed? Or did persistent persecution drive him to take him own life?Alan Turing: Unlocking the Enigma seeks to find the man behind the science, illuminating the life of a person who is still a shadowy presence behind his brilliant achievements.

    

    The author has made this edition more accessible to better meet the needs of today's undergraduate mathematics and philosophy students. It is intended for the reader who has not studied logic previously, but who has some experience in mathematical reasoning. Material is presented on computer science issues such as computational complexity and database queries, with additional coverage of introductory material such as sets.

    Students know that Schaum's delivers the goods—in faster learning curves,better test scores,and higher grades!If you don't have a lot of time but want to excel in class,this book helps you: Brush up before tests; Find answers fast; Study quickly and more effectively; Get the big picture without spending hours poring over dull texts Schaum's Outlines give you the information teachers expect you to know in a handy and succinct format—without overwhelming you with unnecessary details. You get a complete overview of the subject—and no distracting minutiae. Plus,you get plenty of practice exercises to test your skill. Compatible with any classroom text,Schaum's lets you study at your own pace and reminds you of all the important facts you need to remember—fast! And Schaum's is so complete it's the perfect tool for preparing for graduate or professional exams! Students of mathematical economics apply complex formulas—a challenging task that even the best students find daunting. But this Schaum's guide demystifies tough problems and gives you plenty of fully worked examples! Chapters include: Review. Economic Applications of Graphs and Equations. The Derivative and the Rules of Differentiation. Uses of the Derivative in Mathematics and Economics. Calculus of Multivariable Functions. Calculus of Multivariable Functions in Economics. Exponential and LogarithmicFunctions. Exponential and Logarithmic Functions in Economics. Differentiation of Exponential and Logarithmic Functions. The Fundamentals of Linear (or Matrix) Algebra. Matrix Inversion. Special Determinants and Matrices and Their Use in Economics. Linear Programming: A Graphic Approach. Linear Programming: The Simplex Algorithm. Linear Programming: The Dual. Integral Calculus: The Indefinite Integral. Integral Calculus: The Definite Integral. Differential Equations. Difference Equations. Second-Order Differential Equations and Difference Equations. The Calculus of Variations

    How do scientists look at chance, or randomness, and chaos in physical systems? In answering this question for a general audience, Ruelle writes in the best French tradition: he has produced an authoritative and elegant book--a model of clarity, succinctness, and a humor bordering at times on the sardonic.

    The book begins with relevant scientific fundamentals and progresses through an exploration of the solar system, stars, galaxies, and cosmology. The Astronomy textbook builds student understanding through the use of relevant analogies, clear and non-technical explanations, and rich illustrations. Mathematics is included in a flexible manner to meet the needs of individual instructors.

    The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before.But our love of computers should not cloud our thinking about their limitations.We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are uselessin judging whether the unearthed patterns are sensible because computers do not think the way humans think.We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us.The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.