Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms

A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming-anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.

"...offers an introduction to the fundamentals of regression analysis, focusing on the fitting & checking of both linear & nonlinear regression models with computers/calculators... includes carefully designed exercise sets, computer disk."

"An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians."--Publisher's information

The standard-setting textbook in neurochemistry is now in its thoroughly updated Sixth Edition. All chapters have been extensively revised, and new chapters by new contributors cover cell-cell interactions; adhesion molecules and extracellular matrix; intracellular trafficking; cytosol-nuclear communication; nerve growth and regeneration; excitotoxicity; apoptosis; drug addiction; and prion diseases. Molecular biology is integrated into every chapter and the neurochemical basis of disease is discussed when it is known. More than 500 illustrations, over 400 in color, complement the text.Basic Neurochemistry, Sixth Edition is available on a CD-ROM that includes links to the MEDLINE(R) database and the Basic Neurochemistry Website. A slide set of illustrations from the book is also available. See Media Products Section for details.

The book contains predominantly two-color illustrations, with some black-and-white and color illustrations.

Hardbound. The chapters in this volume, written by specialists in computer science and statistics, illustrate the trend in modern statistics of basic methodology supported by the state-of-the-art computational and graphical facilities, and their applications to diverse fields of human endeavour. Specifically, the Handbook is designed to serve as a practical guide to consulting statisticians; to provide research workers with an overview of current developments in computing and indicate their possible use in statistical work; to bring the latest developments in certain areas of computing and demands for the future to the attention of computer scientists; and to promote an interface between statisticians and computer scientists for the benefit of both. This work is a valuable guide to computer scientists, statistical consultants, computer programmers and research workers in all fields involved in data analysis. Foundations Of Multiple Regression Analysis -- Regression Analysis With Categorical And Continuous Variables -- Causal Analysis -- Multivariate Analysis. Elazar J. Pedhazur. Previous Ed. Published In 1973 As: Multiple Regression In Behavioral Research / Fred N. Kerlinger, Elazar J. Pedhazur. Includes Indexes. Bibliography: P. 794-811. A major goal of scientific exploration is the discovery of relationships among variables. Regression is the analysis or measure of the relationship between a dependent variable and one or more independent variables. This text covers a commonly used statistical tool in constructing mathematical models from experimental data. Despite the advent of molecular genetics in neurobiology, our understanding of the functional relationships of the components of the central nervous system (CNS) remains in its infancy, particularly in the areas of cellular interaction and synaptic modulation. Illustrations by Lorie M. Gavulic, MFA Sponsored by the American Society for Neurochemistry