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Introducing many innovations in content and methods, this book involves the foundations, basic concepts, and fundamental results of probability theory. Geared toward readers seeking a firm basis for study of mathematical statistics or information theory, it also covers the mathematical notions of experiments and independence. 1970 edition.
Designed to serve as a basic text for an introductory course in Public Administration, this innovative work provides students with an understanding of the basic management functions that are covered in all standard textbooks with two important differences. First, it is written to address the needs of both the experienced practitioner and the entry-level public servant. Case examples bridge the content-rich environment of practitioners with the basic principles of public administration sought by pre-service students. Second, the discussion of basic management practices is grounded in the political and ethical tensions inherent in the American constitutional form of governance. This reflects the authors' belief that public administration operates as an integral part of the country's political traditions, and thereby helps define the political culture. The book provides a framework for understanding American political traditions and how they inform public administration as a political practice. Key Changes in the Second Edition include: A new introductory chapter that explains what the authors mean by a constitutional approach and why that is important. An expanded discussion of the role of civil society in promoting the common good. A new section in chapter 5 on New Public Governance. Updated exhibits that incorporate up-to-date census data and revenue figures (chapter 10). A new section in chapter 14 that recognises the importance of maintaining accountability in contract and networked systems of governance. Significantly rewritten chapters to add emphasis on the relevance of the chapter material to nonprofit organisations. A significantly revised bibliography which incorporates new bodies of research that have appeared since the first edition.
This book is directed toward several audiences. First, it is designed for university courses in HRD. We argue that every HRD academic program needs a course that teaches the foundations of the field. Second, HRD researchers will find the book thought-provoking and useful as a guide to core research issues. Third, it is written for reflective practitioners who actively seek to lead the field as it grows and matures. Finally, almost every practitioner will find parts of the book that will add depth to their practice.
In this pathbreaking study of foundation influence, author Joan Roelofs produces a comprehensive picture of philanthropy's critical role in society. She shows how a vast number of policy innovations have arisen from the most important foundations, lessening the destructive impact of global "marketization." Conversely, groups and movements that might challenge the status quo are nudged into line with grants and technical assistance, and foundations also have considerable power to shape such things as public opinion, higher education, and elite ideology. The cumulative effect is that foundations, despite their progressive goals, have a depoliticizing effect, one that preserves the hegemony of neoliberal institutions.
Foundations of Low Vision: Clinical and Functional Perspectives, the ground-breaking text that highlighted the importance of focusing on the functional as well as the clinical implications of low vision, has been completely updated and expanded in this second edition. The revised edition goes even further in its presentation of how best to assess and support both children and adults with low vision and plan programs and services that optimize their functional vision and ability to lead productive and satisfying lives, based on individuals' actual abilities. Part 1, Personal and Professional Perspectives, provides the foundations of this approach, with chapters focused on the anatomy of the eye, medical causes of visual impairment, optics and low vision devices, and clinical low vision services, as well as psychological and social implications of low vision and the history of the field. Part 2 focuses on children and youths, providing detailed treatment of functional vision assessment, instruction, use of low vision devices, orientation and mobility, and assistive technology. Part 3 presents rehabilitation and employment issues for working-age adults and special considerations for older adults.
Preface. Introduction: Why Study Foundations of Music Education? 1. History of Music Education. 2. Philosopbical Foundations of Music Education. 3. The Musical and Aesthetic Foundations of Music Education. 4. The Role and Purpose of Music in American Education. 5. Sociological Foundations of Music Education. 6. Social Psychological Foundations of Music Education. 7. Psychological Foundations of Music Education. 8. Application of Psychology to Music Teaching. 9. Curriculum. 10. Assessing Musical Behaviors. 11. Research and Music Education. 12. Teacher Education and Future Directions. Index.
An easy and fun approach to teaching your child to read A stand-alone phonics & reading program, flexible for use in either kindergarten or first gradeA workbook filled with lessons, instructions, and suggested hands-on activities for a full semester of studySuggested hands-on activities will utilize materials typically found in the kitchen or playroom (e.g., crayons, markers, flour, rice, play-dough) This unique curriculum will take your student on a journey from the beginning of creation to the Resurrection of Christ as they learn each letter and corresponding sound. Designed to meet the needs of students who are ready to begin writing, as well as those who may not have mastered the hand-eye coordination skills yet that are needed for writing.
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.