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Machine Learning Methods
This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods.It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields.In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining.In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics.The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
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Methods in iPSC Technology
The series Advances in Stem Cell Biology is a timely and expansive collection of comprehensive information and new discoveries in the field of stem cell biology.Methods in iPSC Technology, Volume 9 addresses the different methods used for induced pluripotent stem cell (iPSC) formation, maintenance, expansion, and differentiation.The ability to reprogram different cell types to iPSCs offers an opportunity to generate pluripotent patient-specific cell lines that can help in the understanding of multiple human disorders.This volume addresses a variety of current methods used in the generation and manipulation of iPSCs, such as magnetic nanoparticles, piggyBac vectors, lentiviral vectors, bioscaffolds, somatic cell nuclear transfer, CRISPR/Cas9, bacteria, and much more.This volume is written for researchers and scientists interested in stem cell therapy, cell biology, regenerative medicine, and organ transplantation and is contributed by world-renowned authors in the field.
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Ensemble Methods for Machine Learning
Many machine learning problems are too complex to be resolved by a single model or algorithm.Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem.By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications.Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning.It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone.This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.
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Automated Machine Learning : Methods, Systems, Challenges
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge.However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters.To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
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Re-theorising Learning and Research Methods in Learning Research
Re-Theorising Learning and Research Methods in Learning Research explores the latest developments in the field of learning theory, offering an overview of emerging methods and demonstrating how recent research contributes to furthering understanding of learning.This book illustrates how theory and methods inform one another, facilitating advancements in the field, while addressing the ways in which societal and technological change create a need for adapting approaches to examining learning.Drawing on an international team of contributors, this book comprises 17 chapters and three commentaries, thematically organised into three broad sections:emerging theories and conceptualisations of learning and how they drive methodological developmentnew methods or innovative use of existing methods and their contribution to theory developmenttheories and methods that emerge in connection with societal changesBoth novice researchers and more experienced scholars will benefit from an overview of recent theoretical and methodological advances in the learning research field.This is an invaluable resource for researchers in the learning and educational research field and will also support Masters and PhD students to understand how learning theories and research methodology in the field have been evolving in recent years.
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Innovation Project Management : Methods, Case Studies, and Tools for Managing Innovation Projects
INNOVATION PROJECT MANAGEMENT ACTIONABLE TOOLS, PROCESSES, AND METRICS FOR SUCCESSFULLY MANAGING INNOVATION PROJECTS, WITH EXCLUSIVE INSIGHTS FROM WORLD-CLASS ORGANIZATIONS AROUND THE WORLD The newly revised Second Edition of Innovation Project Management offers students and practicing professionals the tools, processes, and metrics needed to successfully manage innovation projects, providing value-based innovation project management metrics as well as guidance for how to establish a metrics management program.The highly qualified author analyzes innovation from all sides; through this approach, Innovation Project Management breaks down traditional project management methods and explains why and how innovation projects should be managed differently.The Second Edition includes exclusive insights from world-class organizations such as IBM, Hitachi, Repsol, Philips, Deloitte, IdeaScale, KAUST, and more.It includes six all new case studies, featuring a dive into brand management innovation from Lego.Each case study contains questions for discussion, and instructors have access to an Instructor’s Manual via the book’s companion website.Specific ideas discussed in Innovation Project Management include: Continuous versus discontinuous innovation, incremental versus radical innovation, understanding innovation differences, and incremental innovation versus new product developmentIdentifying core competencies using SWOT analysis and nondisclosure agreements, secrecy agreements, and confidentiality agreementsImplications and issues for project managers and innovation personnel, active listening, pitching the innovation, and cognitive biasesMeasuring intangible assets, customer/stakeholder impact on value metrics, customer value management programs, and the relationship between project management and value With its highly detailed and comprehensive coverage of the field, and with case studies from leading companies to show how concepts are applied in real-world situations, Innovation Project Management is a must-have title for practicing project managers, as well as students in project management, innovation, and entrepreneurship programs.
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Explainable Deep Learning AI : Methods and Challenges
Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems.The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches.It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence. The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
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Adaptive Learning Methods for Nonlinear System Modeling
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification.Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice.This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system.By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system.In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors.Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence.This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems.
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