Fashion and style are no longer subjects that are passed down from father to son, and any man who suddenly decides that he wants to look better is often intimidated and overwhelmed. Most men's fashion books are overly-preachy and judgemental; they try to dress men in a very conservative style that may not actually match their personality or tastes."Style for Men: A simple guide to dressing well" is designed for men who want to understand the fundamental rules of men's style; how to tell if clothing fits, how to discern between 'good' and 'bad' garments and how to create a style that matches your personality, your job and your lifestyle. It's easy-to-follow format features simple and clear illustrations, specifically designed for the Kindle. It even details the best way for men to shop for clothes effectively - from choosing the right stores to selecting garments and dealing with sales assistants.

    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.

    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.

    Earth has been remade into a lush, thriving ecosystem, but with a new dominant species: the machines.These massive, animal-like robots fill the lands, oceans, and skies, serving as the guardians and enforcers of the revived natural order.New generations of humans formed into pre-industrial tribes, without knowledge of the doomed civilization that preceded them, that of the "Old Ones" – us.Little did they know that threats from the ancient world persisted, the greatest of which was HADES, a mysterious A.I. bent on wiping out all organic life. Bolstered by an army of misguided zealots and corrupted machines, it launched a massive assault on humanity's largest tribe.After a desperate battle, HADES was defeated by Aloy, the greatest machine hunter of her age, and a coalition of faithful allies at the city of Meridian.Now Talanah, one of Aloy's closest confidantes and the newly appointed Sunhawk of the Hunters Lodge, seeks a moment of respite after the epic struggle.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    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.

    Be sure to look inside the book to get a free sample of this quality product!

    Some people will adore the grainy texture of a pear, while others will shudder at the idea of this texture in their mouths. Touching a feather boa will be fun and luxurious to some, and others will bristle at the idea of all those feathers brushing on the skin. Noisy, busy environments will energize some people, and will overwhelm others.The author identifies four major sensory types: Seekers; Bystanders; Avoiders and Sensors. Readers can use the questionnaire to find their own patterns and the patterns of those around them, and can benefit from practical sensory ideas for individuals, families and businesses.Armed with the information in Living Sensationally, people will be able to pick just the right kind of clothing, job and home and know why they are making such choices.

    These areas of knowledge have converged into a modern theory of epidemiology that has been slow to penetrate into textbooks, particularly at the introductory level. Epidemiology: An Introduction closes the gap. It begins with a brief, lucid discussion of causal thinking and causal inference and then takes the reader through the elements of epidemiology, focusing on the measures of disease occurrence and causal effects. With these building blocks in place, the reader learns how to design, analyze and interpret problems that epidemiologists face, including confounding, the role of chance, and the exploration of interactions. All these topics are layered on the foundation of basic principles presented in simple language, with numerous examples and questions for further thought.

    This informative and indispensable reference reviews fundamental information about fluids, electrolytes, and acid-base balance; identifies electrolyte, fluid, acid, and base imbalances; describes imbalances in major health problems and their consequences; and explains how to treat those imbalances—all in an easy-to-understand, comprehensive, enjoyable format.

    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.

    Edited by Drs. Karen Marcdante, Robert M. Kliegman, Hal B. Jenson, and Richard E. Behrman, this edition's content was specifically developed in accordance with the 2009 curriculum guidelines of the Council on Medical Student Education in Pediatrics. It also includes many new and improved clinical photographs and images for enhanced visual reference. A user-friendly full-color format and online access via Student Consult facilitate study and expedite reference.

    The authors believe that an effective managerial economics book must go beyond the nuts and bolts of economic analysis to show how these economic analysis techniques are used by practicing managers.

    Fully updated and revised to deal with recent changes to the GMAT, they were designed with a content-based approach.The Critical Reasoning Guide demystifies critical reasoning by teaching a clear, consistent, and effective approach to understanding an argument’s logic and choosing the best answer to the given question. Unlike other guides that attempt to convey everything in a single tome, the Critical Reasoning Strategy Guide is designed to provide deep, focused coverage of one specialized area tested on the GMAT. As a result, students benefit from thorough and comprehensive subject material, clear explanations of fundamental principles, and step-by-step instructions of important techniques. In-action practice problems and detailed answer explanations challenge the student, while topical sets of Official Guide problems provide the opportunity for further growth. Used by itself or with other Manhattan GMAT Strategy Guides, the Critical Reasoning Guide will help students develop all the knowledge, skills, and strategic thinking necessary for success on the GMAT. Purchase of this book includes one year of access to Manhattan GMAT’s online computer-adaptive practice exams and Critical Reasoning Question Bank. All of Manhattan Prep’s GMAT Strategy Guides are aligned with both the 2015 Edition and 13th Edition GMAC Official Guide.

    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