Now William Dunham gives them the attention they deserve.Dunham places each theorem within its historical context and explores the very human and often turbulent life of the creator — from Archimedes, the absentminded theoretician whose absorption in his work often precluded eating or bathing, to Gerolamo Cardano, the sixteenth-century mathematician whose accomplishments flourished despite a bizarre array of misadventures, to the paranoid genius of modern times, Georg Cantor. He also provides step-by-step proofs for the theorems, each easily accessible to readers with no more than a knowledge of high school mathematics.A rare combination of the historical, biographical, and mathematical, Journey Through Genius is a fascinating introduction to a neglected field of human creativity.

    You'll explore a wide range of important Java topics as you systematically learn how to pass the certification exam. Each chapter starts with a list of the exam objectives covered in that chapter. You'll find sample questions and exercises designed to reinforce key concepts and to prepare you for what you'll see in the real exam, along with numerous tips, notes, and visual aids throughout the book.About This BookTo earn the OCA Java SE 7 Programmer Certification, you need to know your Java inside and out, and to pass the exam it's good to understand the test itself. This book cracks open the questions, exercises, and expectations you'll face on the OCA exam so you'll be ready and confident on test day.OCA Java SE 7 Programmer I Certification Guide is a comprehensive guide to the 1Z0-803 exam. You'll explore important Java topics as you systematically learn what is required. Each chapter starts with a list of exam objectives, followed by sample questions and exercises designed to reinforce key concepts. It provides multiple ways to digest important techniques and concepts, including analogies, diagrams, flowcharts, and lots of well-commented code.Written for developers with a working knowledge of Java who want to earn the OCA Java SE 7 Programmer I Certification.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.What's InsideCovers all exam topicsHands-on coding exercisesHow to avoid built-in traps and pitfallsAbout the AuthorMala Gupta has been training programmers to pass Java certification exams since 2006. She holds OCA Java SE7 Programmer I, SCWCD, and SCJP certifications.Table of ContentsIntroductionJava basicsWorking with Java data typesMethods and encapsulationString, StringBuilder, Arrays, and ArrayListFlow controlWorking with inheritanceException handlingFull mock exam

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Students are provided with an exceptionally clear and coherent understanding of child development, emphasizing the interrelatedness of all domains physical, cognitive, emotional, and social throughout the text narrative and in special features. Focusing on education and social policy as critical pieces of the dynamic system in which the child develops, Berk pays meticulous attention to the most recent scholarship in the field. Berk helps students connect their learning to their personal and professional areas of interest and their future pursuits as parents, educators, heath care providers, counselors, social workers, and researchers."

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    His Contemporary Abstract Algebra, 6/e, includes challenging topics in abstract algebra as well as numerous figures, tables, photographs, charts, biographies, computer exercises, and suggested readings that give the subject a current feel and makes the content interesting and relevant for students.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Written by two of the field's true pioneers, Spacetime Physics can extend and enhance coverage of specialty relativity in the classroom. This thoroughly up-to-date, highly accessible overview covers microgravity, collider accelerators, satellite probes, neutron detectors, radioastronomy, and pulsars.  The chapter on general relativity with new material on gravity waves, black holes, and cosmology.

    It demonstrates how to encourage, develop, and foster the processes which seem to come naturally to mathematicians.

    This book provides an introduction to the field of solid state physics for undergraduate students in physics, chemistry, engineering, and materials science.

    

    DLC: Quantum theory.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

     Using case examples of both good and bad student practice, the handbook takes students through each step of the dissertation process, from their initial research proposal to the final submission. The author uses clear illustrations of what students need to do - or not do - to reach their potential, helping them to avoid the most common pitfalls. This essential handbook covers: Producing focused and relevant research objectives Writing your literature review Citing your sources correctly Clearly explaining your use of research methods Writing up your findings Summarizing your work by linking your conclusions to your initial proposal Understanding marking schemes Aimed primarily at Master's students or students on short postgraduate courses in business, humanities and the social sciences, this book is also key reading for supervisors and undergraduates considering postgraduate study.

    In addition to step-by-step coverage of documentation, the book includes an overview of the research-writing process entitled "Writing Scholarly Papers" and three useful appendices. Thorough and practical, this convenient reference guide is also less expensive and easier for undergraduates to use than the APA Manual. The Second Edition features expanded coverage of electronic sources to keep students up-to-date on using and evaluating Internet references in their research. In addition, this new edition provides more guidance on avoiding plagiarism. The two sample APA-style papers--one argumentative and one experimental--are carefully annotated to give students extra support as they master the elements of manuscript preparation and documentation principles.