Here are the refereed proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2006, held in Dublin, Ireland, June 2006. The book presents 22 revised full papers and 19 revised short papers together with abstracts of 3 keynotes, 12 poster papers, and 14 doctoral consortium posters. Topics include pioneering theories, techniques, and innovative technologies to provide dynamic personalization, adaptation, and contextualization of hypermedia resources and services.
Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stackAbout This Book- Master the theory and algorithms behind numerical recipes and how they can be applied to real-world problems- Learn to combine the most appropriate built-in functions from the SciPy stack by understanding the connection between the sources of your problem, volume of data, or computer architecture- A comprehensive coverage of all the mathematical techniques needed to solve the presented topics, with a discussion of the relevant algorithms built in the SciPy stackWho This Book Is ForIf you are a mathematician, engineer, or computer scientist with a proficiency in Python and familiarity with IPython, this is the book for you. Some basic knowledge of numerical methods in scientific computing would be helpful.What You Will Learn- Master relevant algorithms used in symbolic or numerical mathematics to address approximation, interpolation, differentiation, integration, root-finding, and optimization of scalar or multi-variate functions- Develop different algorithms and strategies to efficiently store and manipulate large matrices of data, in particular to solve systems of linear equations, or compute their eigenvalues/eigenvectors- Understand how to model physical problems with systems of differential equations and distinguish the factors that dictate the strategies to solve them- Perform statistical analysis, hypothesis test design and resolution, or data mining at a higher level, and apply them to real-life problems in the field of data analysis- Gain insights on the power of distances, Delaunay triangulations and Voronoi diagrams for Computational Geometry, and apply them to various engineering problems- Familiarize yourself with different techniques in signal/image processing, including filtering audio, images, or video to extract information, features, or remove componentsIn DetailThe SciPy stack is a collection of open source libraries of the powerful scripting language Python, together with its interactive shells. This environment offers a cutting-edge platform for numerical computation, programming, visualization and publishing, and is used by some of the world's leading mathematicians, scientists, and engineers. It works on any operating system that supports Python and is very easy to install, and completely free of charge! It can effectively transform into a data-processing and system-prototyping environment, directly rivalling MATLAB and Octave.This book goes beyond a mere description of the different built-in functions coded in the libraries from the SciPy stack. It presents you with a solid mathematical and computational background to help you identify the right tools for each problem in scientific computing and visualization. You will gain an insight into the best practices with numerical methods depending on the amount or type of data, properties of the mathematical tools employed, or computer architecture, among other factors.The book kicks off with a concise exploration of the basics of numerical linear algebra and graph theory for the treatment of problems that handle large data sets or matrices. In the subsequent chapters, you will delve into the depths of algorithms in symbolic algebra and numerical analysis to address modeling/simulation of various real-world problems with functions (through interpolation, approximation, or creation of systems of differential equations), and extract their representing features (zeros, extrema, integration or differentiation).Lastly, you will move on to advanced concepts of data analysis, image/signal processing, and computational geometry.Style and approachPacked with real-world examples, this book explores the mathematical techniques needed to solve the presented topics, and focuses on the algorithms built in the SciPy stack.
Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problemsAbout This Book- Understand the principles of Bayesian Inference with less mathematical equations- Learn state-of-the art Machine Learning methods- Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guideWho This Book Is ForThis book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R.What You Will Learn- Set up the R environment- Create a classification model to predict and explore discrete variables- Get acquainted with Probability Theory to analyze random events- Build Linear Regression models- Use Bayesian networks to infer the probability distribution of decision variables in a problem- Model a problem using Bayesian Linear Regression approach with the R package BLR- Use Bayesian Logistic Regression model to classify numerical data- Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computingIn DetailBayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.Style and approachThe book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.