Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Plus, it helps you identify areas of weakness so you can focus your study time. Sample problems and step-by-step examples with explanations in the math and physics sections show you how to work through each problem so you understand the steps it takes to complete the equation. Practice tests with answer keys for each topic - located in the appendices for quick access - help you assess your understanding of each topic and familiarize you with the types of questions you're likely to encounter on the actual exam. HESI Hints boxes offer valuable test-taking tips, as well as rationales, suggestions, examples, and reminders for specific topics.End-of-chapter review questions help you gauge your understanding of chapter content.A full-color layout and more illustrations in the life science chapters visually reinforce key concepts for better understanding.Expanded and updated content in each chapter ensures you're studying the most current content.Basic algebra review in the math section offers additional review and practice.Color-coded chapters help you quickly find specific topic sections.Helpful organizational features in each chapter include an introduction, key terms, chapter outline, and a bulleted chapter summary to help you focus your study.A glossary at the end of the text offers quick access to key terms and their definitions.

    

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Anyone who wants to understand these complex and dynamic institutions—how they are evolving, the contributions they make, the challenges they face, the students they serve, and the faculty and leaders who deliver the services and the curricula—will find The American Community College both essential reading and an important reference book."—George R. Boggs, president and CEO, American Association of Community Colleges"I have been a community college president for over forty-one years and a graduate professor for three decades. This book has been an inspiration to generations of students, faculty members, and administrators. It has become the classic of the field because it has great 'take-home' value to us all."—Joseph N. Hankin, president, Westchester Community College"In this latest edition of The American Community College, the authors continue to manifest their unique, highly knowledgeable perspective about the community college. This book is must-reading for all who desire to understand one of the most important educational institutions in the twenty-first century."—Barbara K. Townsend, professor and director, Center for Community College Research, College of Education, University of Missouri–Columbia"Cohen and Brawer's classic work is the touchstone for a comprehensive overview of the American community college. This is a seminal book for graduate students as well as seasoned professionals for understanding this uniquely American institution."—Charles R. Dassance, president, Central Florida Community College

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    The book covers habituation, classical conditioning, instrumental conditioning, stimulus control, aversive control, and their applications to the study of cognition and to the alleviation of behavior problems. Biological constraints on learning are integrated throughout the text, as are applications boxes that relate animal research to human learning and behavior. The book closely reflects the field of research it represents in terms of topics covered, theories discussed, and experimental paradigms described.

    Combining psychological support with a project management approach that breaks tasks into small, manageable chunks, experienced dissertation coach Alison Miller shows you how to overcome negativity and succeed in completing your dissertation beyond your own expectations.

    Indispensable reference for graduates and undergraduates.

    Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for under $10.The future for OpenIntro depends on the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide free course management tools, including an online question bank, utilities for creating course quizzes, and many other helpful resources.CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON SERVICES LLC. THIS CONTENT IS PROVIDED ‘AS IS’ AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME.Can’t find it here? Search Amazon.com Search: All Products Apparel & AccessoriesBabyBeautyBooksCamera & PhotoCell Phones & ServiceClassical MusicComputersComputer & Video GamesDVDElectronicsGourmet FoodHome & GardenMiscellaneousHealth & Personal CareJewelry & WatchesKitchen & HousewaresMagazine SubscriptionsMusicMusical InstrumentsSoftwareSports & OutdoorsTools & HardwareToys & GamesVHS Keywords:

    A host of carefully crafted pedagogical aids support both the succinctly written narrative as well as the outstanding illustration program. Clinical applications, popular with students, represent a variety of clinical perspectives and provide both relevance and motivation for students as they study. The ninth edition of the text continues to break new ground in a modern day anatomy textbook by offering students a glimpse into the fascinating history of how we have come to know what we do, opportunities to explore structure from diverse perspectives, and insights into the vital contributions that anatomical knowledge brings to the understanding of functions and diagnosis and treatment of disease.

    It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity. Topics include sets, logic, counting, methods of conditional and non-conditional proof, disproof, induction, relations, functions and infinite cardinality.

    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.

    A bachelor’s degree is now required for entry into a growing number of professions. And some parents begin planning for the expense of sending their kids to college when they’re born. Almost everyone strives to go, but almost no one asks the fundamental question posed by Academically Adrift: are undergraduates really learning anything once they get there?For a large proportion of students, Richard Arum and Josipa Roksa’s answer to that question is a definitive no. Their extensive research draws on survey responses, transcript data, and, for the first time, the state-of-the-art Collegiate Learning Assessment, a standardized test administered to students in their first semester and then again at the end of their second year. According to their analysis of more than 2,300 undergraduates at twenty-four institutions, 45 percent of these students demonstrate no significant improvement in a range of skills—including critical thinking, complex reasoning, and writing—during their first two years of college. As troubling as their findings are, Arum and Roksa argue that for many faculty and administrators they will come as no surprise—instead, they are the expected result of a student body distracted by socializing or working and an institutional culture that puts undergraduate learning close to the bottom of the priority list.Academically Adrift holds sobering lessons for students, faculty, administrators, policy makers, and parents—all of whom are implicated in promoting or at least ignoring contemporary campus culture. Higher education faces crises on a number of fronts, but Arum and Roksa’s report that colleges are failing at their most basic mission will demand the attention of us all.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.