Libros de Informática práctica, página 18 de 348

Recent years have seen an explosion of work on machine learning, the computational study of algorithms that improve performance based on experience.Research on rule induction, neural networks, genetic algorithms, case-based reasoning, and probabilistic inference has produced a variety of robust methods for inducing knowledge from training data.This book covers the main induction algorithms explored in the literature and presents them within a coherent theoretical framework that moves beyond traditional paradigm boundaries. Elements of Machine Learning provides a comprehensive introduction to the fundamental concepts and problems in the field.The book illustrates a variety of basic algorithms for inducing simple concepts from experience, presents alternatives for organizing learned concepts into large-scale structures, and discusses adaptations of the learning methods to more complex problem-solving tasks.The chapters describe these computational techniques in detail and give examples of their operation, along with exercises and references to the literature. This text is suitable for use in graduate courses on machine learning. Researchers and students in artificial intelligence, cognitive science, and statistics will find it a useful and informative addition to their libraries.

The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field. In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI.

'This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. it meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics and useful instructions on writing customized functions' - Jeff Gill, University of Florida, Gainesville

Hackers'' profiles, motivations, tricks, techniques, tools, methodologies, and strategies are revealed in this guide. It also discusses known system vulnerabilities and details how to mount countermeasures fast. The Hacker''s Handbook: The Strategy Behind Breaking Into and Defending Networks, moves ahead of the pack of books about digital security by revealing the technical aspects of hacking that are least understood by network administrators. This is accomplished by analyzing subjects through a hacking/security dichotomy that details hacking maneuvers and defenses in the same context. Chapters are organized around specific technical components and administrative tasks, providing theoretical background that prepares network defenders for the always-changing and creative tools and techniques of intruders. This book is divided into three parts. Part I introduces programming, protocol, and attack concepts. Part II addresses subject areas (protocols, services, technologies, etc.) that may be vulnerable. Part III details consolidation activities that hackers may use following penetration. Each section provides a "path" to hacking/security Web sites and other resources that augment existing content. Referencing these supplemental and constantly-updated resources ensures that this volume remains timely and enduring. By informing IT professionals how to think like hackers, this book serves as a valuable weapon in the fight to protect digital assets.

El libro es un texto de referencia a nivel nacional y latinoamericano en el campo de la Inteligencia artificial y los Sistemas inteligentes (asignatura de posgrado). Ello se consigue mediante la participación de un gran número de autores expertos en cada uno de los temas de distintas universidades españolas, y la coordinación por parte de José Palma y Roque Marín, de la Universidad de Murcia y de la asociación iberoamericana de la Inteligencia Artificial. Cada uno de los temas que componen el libro se desarrollan de forma exhaustiva pero didáctica, plasmando en papel la experiencia de cada uno de los autores en ese campo. La parte teórica se completa con ejercicios de práctica para que el alumno desarrolle los conocimientos adquiridos. El trabajo de los coordinadores es aglutinar y homogeneizar los contenidos de los distintos autores, para lo cual cuentan con una dilatada experiencia en otras obras sobre temas informáticos universitarios. El libro se complementa con una OLC en la que tendrán cabida más materiales complementarios y de práctica desarrollados por los autores y coordinadores.

A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to solve biostatistical problems. Providing extensive breadth, from summary statistics to neural networks, this essential volume offers a comprehensive, step-by-step guide to using JMP to handle your data. The first biostatistical book to focus on software, Biostatistics Using JMP discusses such topics as data visualization, data wrangling, data cleaning, histograms, box plots, Pareto plots, scatter plots, hypothesis tests, confidence intervals, analysis of variance, regression, curve fitting, clustering, classification, discriminant analysis, neural networks, decision trees, logistic regression, survival analysis, control charts, and metaanalysis. Written for university students, professors, those who perform biological/biomedical experiments, laboratory managers, and research scientists, Biostatistics Using JMP provides a practical approach to using JMP to solve your biostatistical problems.