This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
As aplicações de mineração de dados e textos oriundos da Internet têm sido alvo de recentes pesquisas. E, em todos os casos, as tarefas de mineração de dados necessitam trabalhar sobre dados limpos, consistentes e integrados para obter os melhores resultados. Sendo assim, ambientes de Data Warehouse são uma valiosa fonte de dados limpos e integrados para as aplicações de mineração. A tecnologia de Data Warehouse tem evoluído no sentido de recuperar e tratar dados provenientes da Web. Em particular, os sites de notícias são fontes ricas em textos, que podem compor um corpus linguístico. Inserindo o corpus em um ambiente de Data Warehouse, as aplicações poderão tirar proveito da flexibilidade que um modelo multidimensional e as operações OLAP fornecem. Dentre as vantagens estão a navegação pelos dados, a seleção da parte dos dados considerados relevantes, a análise dos dados em diferentes níveis de abstração, e a agregação, desagregação, rotação e filtragem sobre qualquer conjunto de dados. Este trabalho apresenta o ambiente de Data Warehouse Newsminer, que fornece um conjunto de textos consistente e limpo, na forma de um corpus multidimensional.
Turing´s famous 1936 paper introduced a formal definition of a computing machine, a Turing machine. This model led to both the development of actual computers and to computability theory, the study of what machines can and cannot compute. This book presents classical computability theory from Turing and Post to current results and methods, and their use in studying the information content of algebraic structures, models, and their relation to Peano arithmetic. The author presents the subject as an art to be practiced, and an art in the aesthetic sense of inherent beauty which all mathematicians recognize in their subject. Part I gives a thorough development of the foundations of computability, from the definition of Turing machines up to finite injury priority arguments. Key topics include relative computability, and computably enumerable sets, those which can be effectively listed but not necessarily effectively decided, such as the theorems of Peano arithmetic. Part II includes the study of computably open and closed sets of reals and basis and nonbasis theorems for effectively closed sets. Part III covers minimal Turing degrees. Part IV is an introduction to games and their use in proving theorems. Finally, Part V offers a short history of computability theory. The author has honed the content over decades according to feedback from students, lecturers, and researchers around the world. Most chapters include exercises, and the material is carefully structured according to importance and difficulty. The book is suitable for advanced undergraduate and graduate students in computer science and mathematics and researchers engaged with computability and mathematical logic.
Monograph considers a problem of analysis of personal medical data and describes the application of machine learning technology and Big data to solve the problem of personalized approach in the tasks of making medical decisions. This information to develop innovative approaches to risk forecasting, modeling therapies, and improving the quality of medical care by personalizing treatment schemes of patients is used. And it will also allow you to effectively optimize data processing even when new information revenues come from different sources. Also this monograph describes the verifying methods of medical specialty from user profile of online community for health-related advices, actual fuzzy logic approach for modelling the behavior classification of social news aggregations users and the analysis of personal patient information whisch is one of critical factors for analysis of medical data. In medical and biological research, as well as in practical medicine, the range of tasks to be solved is so wide that it is possible to use any of the methodologies of machine learning and Big Data.
Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen ´´Analytics´´-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.
Without going into too much detail here, in this humble work we are going to recover the best method to check the employee´s attendance at workplace using a biometric machine which uses the biological attributes and characterizes of the given person to attain more reliable verification or identification. Every employer knows how much is important to monitor their employees to reduce the absenteeism and tardiness for enhancing the productivity and performance. There are dozens of solutions which simplify to monitor employees in their different conditions which including (Position tracking, telephone tapping, Employee monitoring software and fingerprinting). These characteristics should not be duplicable, but it is unfortunately often possible to create a copy that is accepted by the biometric system as a true sample. Biometric authentication uses features of your personal physiology, like as your retinal image or fingerprint, to identify you as you. The technology is mainly used for identification and access control, or for identifying individuals that are under surveillance and it used for verification and authentication.
Work with blockchain and understand its potential application beyond cryptocurrencies in the domains of healthcare, Internet of Things, finance, decentralized organizations, and open science. Featuring case studies and practical insights generated from a start-up spun off from the author´s own lab, this book covers a unique mix of topics not found in others and offers insight into how to overcome real hurdles that arise as the market and consumers grow accustomed to blockchain based start-ups. You´ll start with a review of the historical origins of blockchain and explore the basic cryptography needed to make the blockchain work for Bitcoin. You will then learn about the technical advancements made in the surrounded ecosystem: the Ethereum virtual machine, Solidity, Colored Coins, the Hyperledger Project, Blockchain-as-a-service offered through IBM, Microsoft and more. This book looks at the consequences of machine-to-machine transactions using the blockchain socially, technologically, economically and politically. Blockchain Enabled Applications provides you with a clear perspective of the ecosystem that has developed around the blockchain and the various industries it has penetrated. What You´ll Learn Implement the code-base from Fabric and Sawtooth, two open source blockchain-efforts being developed under the Hyperledger Project Evaluate the benefits of integrating blockchain with emerging technologies, such as machine learning and artificial intelligence in the cloud Use the practical insights provided by the case studies to your own projects or start-up ideas Set up a development environment to compile and manage projects Who This Book Is For Developers who are interested in learning about the blockchain as a data-structure, the recent advancements being made and how to implement the code-base. Decision makers within large corporations (product managers, directors or CIO level executives) interested in implementing the blockchain who need more practical insights and not just theory.
The development of modern high-tech branches of medicine, including orthopaedics, traumatology and dentistry, places high demands on the quality of materials. The study of the processes occurring in the design of biocompatible material and the manufacture of medical products from it, as well as the ability to manage them, contribute to the production of a material with specified properties. So, the task of the optimal biocompatible material selection for medical usage is a complex task that we solved using Artificial Intelligence tools. In the book, authors describe an improved approach to the development of supervised learning methods for high-precision biocompatible materials selection. The general idea of these methods is a compatible use of the Kolmogorov-Gabor polynomial and machine learning algorithms. This polynomial allows increasing the dimension of the input dataset, which in turn increases the likelihood of correct materials classification. Machine learning algorithms are used as fast tools for finding the coefficients of this polynomial. Experimental studies have shown high classification accuracy using the proposed approach.