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.

    Permanent positions are scarce, science survival is rarely part of formal graduate training, and a good mentor is hard to find. This exceptional volume explains what stands between you and fulfilling long-term research career. Bringing the key survival skills into focus, A Ph.D. Is Not Enough! proposes a rational approach to establishing yourself as a scientist. It offers sound advice of selecting a thesis or postdoctoral adviser, choosing among research jobs in academia, government laboratories, and industry, preparing for an employment interview, and defining a research program. This book will help you make your oral presentations effective, your journal articles compelling, and your grant proposals successful. A Ph.D. Is Not Enough should be required reading for anyone on the threshold of a career in science.

    It provides students who lack a strong science background the ideal balance of reliable nutrition information and practical consumer-oriented knowledge.

    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.

    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.

    Over 100 problems at ends of chapters. Answers in back of book. 1962 edition.

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

    This work includes differential forms and the elegant forms of Maxwell's equations, and a chapter on probability and statistics. It also illustrates and proves mathematical relations.

    You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic formats and a self-paced free online course where you can explore the course materials. All the supporting materials for the book are available under open and remixable licenses. This book is designed to teach people to program even if they have no prior experience.

    Their dedication and hard work have yielded an authoritative volume that defines and classifies mental disorders in order to improve diagnoses, treatment, and research.The criteria are concise and explicit, intended to facilitate an objective assessment of symptom presentations in a variety of clinical settings -- inpatient, outpatient, partial hospital, consultation-liaison, clinical, private practice, and primary care. New features and enhancements make DSM-5 easier to use across all settings:- The chapter organization reflects a lifespan approach, with disorders typically diagnosed in childhood (such as neurodevelopmental disorders) at the beginning of the manual, and those more typical of older adults (such as neurocognitive disorders) placed at the end. Also included are age-related factors specific to diagnosis. - The latest findings in neuroimaging and genetics have been integrated into each disorder along with gender and cultural considerations.- The revised organizational structure recognizes symptoms that span multiple diagnostic categories, providing new clinical insight in diagnosis. - Specific criteria have been streamlined, consolidated, or clarified to be consistent with clinical practice (including the consolidation of autism disorder, Asperger's syndrome, and pervasive developmental disorder into autism spectrum disorder; the streamlined classification of bipolar and depressive disorders; the restructuring of substance use disorders for consistency and clarity; and the enhanced specificity for major and mild neurocognitive disorders).- Dimensional assessments for research and validation of clinical results have been provided.- Both ICD-9-CM and ICD-10-CM codes are included for each disorder, and the organizational structure is consistent with the new ICD-11 in development.The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, is the most comprehensive, current, and critical resource for clinical practice available to today's mental health clinicians and researchers of all orientations. The information contained in the manual is also valuable to other physicians and health professionals, including psychologists, counselors, nurses, and occupational and rehabilitation therapists, as well as social workers and forensic and legal specialists.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.

    

    Library and information science students and professionals will find the background and concepts they need to meet today's - and tomorrow's - challenges. TABLE OF CONTENTS: 1. The Information Infrastructure: Libraries in Context; 2. Information Science: A Service Perspective; 3. Redefining the Library: The Impacts and Implications of Technological Change; 4. Information Policy: Stakeholders and Agendas; 5. Information Policy as Library Policy: Intellectual Freedom; 6. Information Organization: Issues and Techniques; 7. From Past to Present: The Library s Mission and Its Values; 8. Ethics and Standards: Professional Practices in Library and Information Science; 9. The Library as Institution: An Organizational View, and 10. Librarianship: An Evolving Profession.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton