Libros de Matemáticas, página 33 de 470

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

TEXTO GENERAL

Este libro difiere de los tradicionales textos de cálculo. Primero, está escrito en un contexto de ciencias, es decir que los conceptos se introducen a través de ejemplos biológicos que enfatizan la importancia que tiene el calculo como herramienta para comprenderlos. Segundo, las ecuaciones diferenciales se presentan al comienzo del libro y se tratan de forma sencilla para que aquellos estudiantes que no tengan una preparacion previa en calculo puedan comprender facilmente. Luego se retoman en capitulos posteriores donde se profundiza mas sobre ellas. Tercero, dada la importancia de estas ecuaciones para el conocimiento de las ciencias biologicas se completa su estudio con aplicaciones actualizadas. Cuarto, las aplicaciones biologicas de diferenciacion e integracion aparecen por todo el texto. Quinto, se ha dedicado todo un capitulo al calculo multivariable. El libro se completa con muchos y nuevos problemas que ayudaran al estudiante a comprender mejor los conceptos que en este texto se presentan.Este libro difiere de los tradicionales textos de calculo.

Howard BaerSupersymmetric models of particle physics predict new superpartner matter states for each particle in the Standard Model. These superpartners will have wide ranging implications, from cosmology to observations at high energy accelerators, such as CERN's LHC. In this text, the authors develop the basic concepts of supersymmetry and show how it can be incorporated into a theoretical framework for describing unified theories of elementary particles. They develop the technical tools of supersymmetry using four-component spinor notation familiar to high energy experimentalists and phenomenologists. The text takes the reader from an abstract formalism to a straightforward recipe for writing supersymmetric gauge theories of particle physics, and ultimately to the calculations necessary for practical applications at colliders and in cosmology. This is a comprehensive, practical and accessible introduction to supersymmetry for experimental and phenomenological particle physicists and graduate students. Exercises and worked examples that clarify the material are interspersed throughout. Develops very general supersymmetric models for the interactions of elementary particles from basic principles Uses 4-component spinor notation to develop the superfield formalism (a necessary technical tool) Extensively treats the experimental implications of supersymmetry Contains over 100 exercises and worked examples throughout the text

Paul M. BellanThis rigorous explanation of plasmas is relevant to diverse plasma applications such as controlled fusion, astrophysical plasmas, solar physics, magnetospheric plasmas, and plasma thrusters. More thorough than previous texts, it exploits new powerful mathematical techniques to develop deeper insights into plasma behavior. After developing the basic plasma equations from first principles, the book explores single particle motion with particular attention to adiabatic invariance. The author then examines types of plasma waves and the issue of Landau damping. Magnetohydrodynamic equilibrium and stability are tackled with emphasis on the topological concepts of magnetic helicity and self-organization. Advanced topics follow, including magnetic reconnection, nonlinear waves, and the Fokker-Planck treatment of collisions. The book concludes by discussing unconventional plasmas such as non-neutral and dusty plasmas. Written for beginning graduate students and advanced undergraduates, this text emphasizes the fundamental principles that apply across many different contexts. Applicable to many different plasma contexts Uses mathematical tools to obtain deeper insights into understanding of behaviour in complex, realistic geometries Contains new, modern topics such as self-organisation, topology of magnetic fields, non-neutral plasmas and dusty plasmas

Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.

TEORÍA Y EJERCICIOS. Este libro constituye el material básico dela asignatura Matemáticas II, que se imparte en el primer curso de Ingeniería Técnica Industrial. Se trata de la segunda parte de la obra "Fundamentos de Matematicas" cuya finalidad es presentar los temas de este disciplina que se estudian a los largo de la carrera. El texto ha sido elaborado teniendo presente que el futuro lector no siempre puede asistir a las tutorias de su Centro Asociado. Hemos pretendido, por un lado, que su contenido sea autosuficiente y, por otro, incluir un gran numero

DOS VOLUMENES: TEORIA Y EJERCICIOS. Esta obra va dirigida a los que por definición "estudian a distancia". Los autores han escrito lo esencial como para que el libro sea llamado, con propiedad, de GEometria Diferencial. Han puesto toda la carne en el asador en el ultimo capitulo, y han desarrollado los demas a un nivel acorde con este ultimo capitulo. Han luchado claramente para lograr precision absoluta en las definiciones, la parte basica de un buen libro, dando interesantisimos contraejemplos que describen sus limites. Las demostraciones que ofrecen son detalladas y rigurosas y en varios caso originales tambien. El libro esta plagado de ejemplos desarrollados hasta el ultimo detalle. Todo ello, completado con agradables notas historicas y muchos ejercicios para proporcionar al lector una ayuda y seguridad que necesita dado la soledad de su estudio. Por otra parte, la 2ª ed. de "Ejercicios de geometria diferencial de curvas y superficies" completa esta 3ª ed. de la teoria e incluye las soluciones a los 60 ejercicios alli propuestos y a otros 240 mas, de ahi que se haya decidido editarlos conjuntamente.

This mid-level text covers mathematics of finance, linear algebra, linear programming, probability, and descriptive statistics, with an emphasis on cross- discipline principles and practices. Student-friendly and accessible, it develops a thorough, functional understanding of mathematical concepts in preparation for their application in other areas. Coverage concentrates on developing concepts and ideas followed immediately by developing computational skills and problem solving

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Cálculo Esencial: Trascendentes tempranas responde a la creciente demanda de un texto más simplificado y rápido para los estudiantes. Ejemplos claros, soluciones trabajadas en varios pasos para ayudar a los estudiantes a aprender conceptos matemáticos complejos. Los ejemplos corresponden a los ejercicios, sirviendo como referencia de apoyo para los estudiantes. Éste es el único texto en el mercado donde cada ejemplo, prueba y explicación comienza y termina en la misma página. Diversos tipos de ejercicios están incluidos en cada conjunto de éstos, involucrando aquellos relacionados con habilidades, pensamiento crítico, solución de problemas, aplicaciones y datos reales. Los ejercicios de escritura sobre conceptos ayudan a los estudiantes a desarrollar una mejor comprensión de los conceptos clave de cada sección. Preguntas por escrito ayudan a los maestros evaluar la capacidad de los estudiantes para explicar conceptos con precisión. Notas integradas en los márgenes en todo el texto mejoran la comprensión del estudiante y ayudan a evitar errores comunes y malos entendidos. Los cuadros de teoremas presentan claramente importantes conceptos matemáticos