Book picks similar to
Introduction to Probability Theory by Paul Gerhard Hoel
statistics
mathematics
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Schaum's Outline of Linear Algebra
Seymour Lipschutz - 1968
This guide provides explanations of eigenvalues, eigenvectors, linear transformations, linear equations, vectors, and matrices.
Understanding Human Behavior and the Social Environment
Charles Zastrow - 1987
Now available with a personalized online learning plan, this social work-specific book looks at lifespan through the lens of social work theory and practice. The authors use an empowerment approach to cover human development and behavior theories within the context of family, organizational, and community systems. Using a chronological lifespan approach, the authors present separate chapters on biological, psychological, and social impacts at the different lifespan stages with an emphasis on strengths and empowerment.
Algorithm Design
Jon Kleinberg - 2005
The book teaches a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.
Symbolic Logic
Irving M. Copi - 1954
The general approach of this book to logic remains the same as in earlier editions. Following Aristotle, we regard logic from two different points of view: on the one hand, logic is an instrument or organon for appraising the correctness of reasoning; on the other hand, the principles and methods of logic used as organon are interesting and important topics to be themselves systematically investigated.
Options, Futures and Other Derivatives
John C. Hull
Changes in the fifth edition include: A new chapter on credit derivatives (Chapter 21). New! Business Snapshots highlight real-world situations and relevant issues. The first six chapters have been -reorganized to better meet the needs of students and .instructors. A new release of the Excel-based software, DerivaGem, is included with each text. A useful Solutions Manual/Study Guide, which includes the worked-out answers to the "Questions and Problems" sections of each chapter, can be purchased separately (ISBN: 0-13-144570-7).
Machine Learning for Hackers
Drew Conway - 2012
Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data
Business Statistics: A First Course
David M. Levine - 1999
Focused more on concepts than on statistical methods, it shows readers how to properly use statistics to analyze data and demonstrates how computer software is an integral part of this analysis. "Using Statistics" scenarios discuss how statistics is used in a real business setting. Includes contemporary business applications, many with real data sets, and an integrated case that runs throughout chapters. "PHSTAT," a custom designed Excel add-in, is packaged with each book. Introduction and Data Collection. Presenting Data in Tables and Charts. Summarizing and Describing Numerical Data. Basic Probability and Probability Distributions. Sampling Distributions and Confidence Interval Estimation. Fundamentals of Hypothesis Testing: One-Sample Tests. Two-Sample and C-Sample Tests with Numerical Data. Hypothesis Testing with Categorical Data. Statistical Applications in Quality and Productivity Management. The Simple Linear Regression Model and Correlations. Introduction to Multiple Regression. Time Series Analysis. An accessible introduction or refresher on statistics for those in accounting, marketing, management, economics, and finance.
Integrating Educational Technology Into Teaching
Margaret D. Roblyer - 1996
It shows teachers how to create an environment in which technology can effectively enhance learning. It contains a technology integration framework that builds on research and the TIP model.
Thomas' Calculus, Early Transcendentals, Media Upgrade
George B. Thomas Jr. - 2002
This book offers a full range of exercises, a precise and conceptual presentation, and a new media package designed specifically to meet the needs of today's readers. The exercises gradually increase in difficulty, helping readers learn to generalize and apply the concepts. The refined table of contents introduces the exponential, logarithmic, and trigonometric functions in Chapter 7 of the text.KEY TOPICS Functions, Limits and Continuity, Differentiation, Applications of Derivatives, Integration, Applications of Definite Integrals, Integrals and Transcendental Functions, Techniques of Integration, Further Applications of Integration, Conic Sections and Polar Coordinates, Infinite Sequences and Series, Vectors and the Geometry of Space, Vector-Valued Functions and Motion in Space, Partial Derivatives, Multiple Integrals, Integration in Vector Fields.MARKET For all readers interested in Calculus.
Machine Learning with R
Brett Lantz - 2014
This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Cultural Anthropology: An Applied Perspective
Gary P. Ferraro - 2007
This contemporary and student-relevant text gives you all the key material you need for your introductory course, plus it will show you that anthropology is for you! With real world applications of the principles and practices of anthropology, this book will help you learn to appreciate other cultures as well as your own. Apply what you learn in this course to those situations that you are likely to encounter in your personal and professional life. What can you do with anthropology today? Check out the real-life examples of cross-cultural misunderstandings and issues (in our popular "Cross-Cultural Miscues" features) to view 'culture at work.' Also, the book takes a look at specialized vocabularies as illustrated by "chickspeak" (the language of single, urban, upwardly mobile women), the war in Iraq, environmental degradation, and other contemporary topics.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Peter Dayan - 2001
This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Digital Image Processing
Rafael C. Gonzalez - 1977
Completely self-contained, heavily illustrated, and mathematically accessible, it has a scope of application that is not limited to the solution of specialized problems. Digital Image Fundamentals. Image Enhancement in the Spatial Domain. Image Enhancement in the Frequency Domain. Image Restoration. Color Image Processing. Wavelets and Multiresolution Processing. Image Compression. Morphological Image Processing. Image Segmentation. Representation and Description. Object Recognition.
Field and Wave Electromagnetics
David K. Cheng - 1982
These include applications drawn from important new areas of technology such as optical fibers, radome design, satellite communication, and microstrip lines. There is also added coverage of several new topics, including Hall effect, radar equation and scattering cross section, transients in transmission lines, waveguides and circular cavity resonators, wave propagation in the ionosphere, and helical antennas. New exercises, new problems, and many worked-out examples make this complex material more accessible to students.
Ordinary Differential Equations
Morris Tenenbaum - 1985
Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.