Learning Management Back From Machines

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Learning Management Back from Machines

Muthukrishnan Kalyanasundaram
Learning Management Back from Machines Book PDF

Author : Muthukrishnan Kalyanasundaram
Publisher : Partridge Publishing
Page : 234 pages
File Size : 44,9 Mb
Release : 2020-12-27
Category : Education
ISBN : 9781482844900

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Learning Management Back from Machines by Muthukrishnan Kalyanasundaram Pdf

Technology driven witty solutions to everyday Managerial Problems Like it is often told “Solutions at your doorstep”, we are completely surrounded by profound managerial solutions waiting to be unearthed from our everyday machines in the form of phones, computers, safety devices, automobile etc. The world of machines abounds with managerial thoughts and solutions. This inspiring book provides us with a new approach in problem solving and addresses the diverse challenges faced in managerial functions today. “Learning Management Back From Machines”, is the wonderful story of Krish and his latest creation, MANU – an advanced hyper-intelligent, direct-neural interface-capable humanoid, which helps Krish along in deriving managerial solutions from fellow-machines and machine-processes alike. In the process of learning and observing the history of various technological marvels along with the need for these inventions, we discover a whole new dimension of creative intelligence and learning, waiting to reveal itself all over again. The book is aimed at understanding the core essence of how machines have been made to work and help us discover new and innovative solutions to our everyday social and managerial problems. • RELIGIONS TEACH US MANAGEMENT. • STORIES AND FABLES TEACH US MANAGEMENT. • MANAGEMENT THEORIES TEACH US MANAGEMENT. • NOW EVERYDAY MACHINES WILL TEACH US MANAGEMENT

Teaching Machines

Audrey Watters
Teaching Machines Book PDF

Author : Audrey Watters
Publisher : MIT Press
Page : 325 pages
File Size : 40,7 Mb
Release : 2023-02-07
Category : Education
ISBN : 9780262546065

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Teaching Machines by Audrey Watters Pdf

How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Learning Management System Technologies and Software Solutions for Online Teaching: Tools and Applications

Kats, Yefim
Learning Management System Technologies and Software Solutions for Online Teaching Tools and Applications Book PDF

Author : Kats, Yefim
Publisher : IGI Global
Page : 486 pages
File Size : 51,5 Mb
Release : 2010-05-31
Category : Technology & Engineering
ISBN : 9781615208548

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Learning Management System Technologies and Software Solutions for Online Teaching: Tools and Applications by Kats, Yefim Pdf

"This book gives a general coverage of learning management systems followed by a comparative analysis of the particular LMS products, review of technologies supporting different aspect of educational process, and, the best practices and methodologies for LMS-supported course delivery"--Provided by publisher.

The LMS Guidebook

Steven D. Foreman
The LMS Guidebook Book PDF

Author : Steven D. Foreman
Publisher : Association for Talent Development
Page : 266 pages
File Size : 55,8 Mb
Release : 2017-12-28
Category : Business & Economics
ISBN : 9781607281658

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The LMS Guidebook by Steven D. Foreman Pdf

Select, Implement, and Operate the Perfect LMS If you need to manage training and education programs for employees, customers, or students, you need an LMS. Don’t waste time and money picking the wrong one. The LMS Guidebook gets to the core of what an LMS does and how it works. This book tackles the urgent challenges you will face when putting an LMS in place: Which features are must-haves? What standards should your LMS comply with to mesh with your other technology systems? How do you migrate existing learning data into your new LMS? How can you ensure an uneventful rollout? Not all LMS products will meet your needs. E-learning consultant Steve Foreman offers a broad view of the LMS categories and features so you can ask better questions of vendors and evaluate their products. He then turns to implementation and operation, offering in-depth guidance on how to establish appropriate standards, processes, and governance that will have your LMS running smoothly. Whether you’re on the instructional or technical side of the LMS, you can make the job of selecting and managing one less painful by following the proven practices in this book.

Machine Learning Applications in Subsurface Energy Resource Management

Srikanta Mishra
Machine Learning Applications in Subsurface Energy Resource Management Book PDF

Author : Srikanta Mishra
Publisher : CRC Press
Page : 379 pages
File Size : 41,7 Mb
Release : 2022-12-27
Category : Technology & Engineering
ISBN : 9781000823875

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Machine Learning Applications in Subsurface Energy Resource Management by Srikanta Mishra Pdf

The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.

Data Mining

Ian H. Witten,Eibe Frank,Mark A. Hall
Data Mining Book PDF

Author : Ian H. Witten,Eibe Frank,Mark A. Hall
Publisher : Elsevier
Page : 665 pages
File Size : 43,5 Mb
Release : 2011-02-03
Category : Computers
ISBN : 9780080890364

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Data Mining by Ian H. Witten,Eibe Frank,Mark A. Hall Pdf

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Utilizing AI and Machine Learning for Natural Disaster Management

Satishkumar, D.,Sivaraja, M.
Utilizing AI and Machine Learning for Natural Disaster Management Book PDF

Author : Satishkumar, D.,Sivaraja, M.
Publisher : IGI Global
Page : 374 pages
File Size : 50,8 Mb
Release : 2024-04-29
Category : Nature
ISBN : 9798369333631

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Utilizing AI and Machine Learning for Natural Disaster Management by Satishkumar, D.,Sivaraja, M. Pdf

Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage.

Machine Learning for Asset Management and Pricing

Henry Schellhorn,Tianmin Kong
Machine Learning for Asset Management and Pricing Book PDF

Author : Henry Schellhorn,Tianmin Kong
Publisher : SIAM
Page : 267 pages
File Size : 53,7 Mb
Release : 2024-03-26
Category : Computers
ISBN : 9781611977905

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Machine Learning for Asset Management and Pricing by Henry Schellhorn,Tianmin Kong Pdf

This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.

Machine Learning for Ecology and Sustainable Natural Resource Management

Grant Humphries,Dawn R. Magness,Falk Huettmann
Machine Learning for Ecology and Sustainable Natural Resource Management Book PDF

Author : Grant Humphries,Dawn R. Magness,Falk Huettmann
Publisher : Springer
Page : 441 pages
File Size : 44,5 Mb
Release : 2018-11-05
Category : Science
ISBN : 9783319969787

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Machine Learning for Ecology and Sustainable Natural Resource Management by Grant Humphries,Dawn R. Magness,Falk Huettmann Pdf

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

AI and Machine Learning for Network and Security Management

Yulei Wu,Jingguo Ge,Tong Li
AI and Machine Learning for Network and Security Management Book PDF

Author : Yulei Wu,Jingguo Ge,Tong Li
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 46,7 Mb
Release : 2022-11-08
Category : Computers
ISBN : 9781119835875

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AI and Machine Learning for Network and Security Management by Yulei Wu,Jingguo Ge,Tong Li Pdf

AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

Artificial Intelligence and Machine Learning in Business Management

Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Artificial Intelligence and Machine Learning in Business Management Book PDF

Author : Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Publisher : CRC Press
Page : 243 pages
File Size : 47,8 Mb
Release : 2021-11-05
Category : Business & Economics
ISBN : 9781000432145

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Artificial Intelligence and Machine Learning in Business Management by Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar Pdf

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

Data Management in Machine Learning Systems

Matthias Boehm,Arun Kumar,Jun Yang
Data Management in Machine Learning Systems Book PDF

Author : Matthias Boehm,Arun Kumar,Jun Yang
Publisher : Springer Nature
Page : 157 pages
File Size : 53,7 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031018695

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Data Management in Machine Learning Systems by Matthias Boehm,Arun Kumar,Jun Yang Pdf

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scalable manner. Data management is at the heart of many ML systems due to data-driven application characteristics, data-centric workload characteristics, and system architectures inspired by classical data management techniques. In this book, we follow this data-centric view of ML systems and aim to provide a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle. We review multiple interconnected lines of work: (1) ML support in database (DB) systems, (2) DB-inspired ML systems, and (3) ML lifecycle systems. Covered topics include: in-database analytics via query generation and user-defined functions, factorized and statistical-relational learning; optimizing compilers for ML workloads; execution strategies and hardware accelerators; data access methods such as compression, partitioning and indexing; resource elasticity and cloud markets; as well as systems for data preparation for ML, model selection, model management, model debugging, and model serving. Given the rapidly evolving field, we strive for a balance between an up-to-date survey of ML systems, an overview of the underlying concepts and techniques, as well as pointers to open research questions. Hence, this book might serve as a starting point for both systems researchers and developers.

Towards an Intelligent Learning Management System Under Blended Learning

Sofia B. Dias,José A. Diniz,Leontios J. Hadjileontiadis
Towards an Intelligent Learning Management System Under Blended Learning Book PDF

Author : Sofia B. Dias,José A. Diniz,Leontios J. Hadjileontiadis
Publisher : Springer Science & Business Media
Page : 235 pages
File Size : 54,7 Mb
Release : 2013-09-29
Category : Technology & Engineering
ISBN : 9783319020785

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Towards an Intelligent Learning Management System Under Blended Learning by Sofia B. Dias,José A. Diniz,Leontios J. Hadjileontiadis Pdf

What are the key channels to change in blended instructional practice as they relate to the use of a learning management system (LMS)? What role LMS users’ profiles play in facilitating change in practice? Can we model users’ quality of interaction (QoI) with LMS? How inclusiveness and affectiveness could lead to a personalized intelligent LMS (iLMS)? If these questions sound intrinsic to you and to your own experience and circumstance, then this book fits absolutely to you. Here, the term Blended – viewed as a fuzzy concept – is understood as a stepping-stone on the way to the future, to explain the multiple ways human beings think/act/feel of society in the 21st century and to embrace the opportunity of humans to re/co-construct new knowledge through the intermediation role of the technology. Initially, based on an online learning environment’ theoretical framework, some current issues of the educational processes in the digital age of Web 2.0 are analyzed. Then, after exploring the main methodological procedures, characteristic examples of research case studies follow, including LMS users’ trends and profiles and modeling of their QoI using fuzzy logic. This book offers useful information that evokes initiatives towards rethinking of the value, efficiency, inclusiveness, affectiveness and personalization of the iLMS-based b-learning environment, both by the educators, the LMS designers and educational policy decision makers.

Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III

A.J. Tallón-Ballesteros
Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III Book PDF

Author : A.J. Tallón-Ballesteros
Publisher : IOS Press
Page : 738 pages
File Size : 53,9 Mb
Release : 2021-12-03
Category : Computers
ISBN : 9781643682259

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Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III by A.J. Tallón-Ballesteros Pdf

It is data that guides the path of applications, and Big Data technologies are enabling new paths which can deal with information in a reasonable time to arrive at an approximate solution, rather than a more exact result in an unacceptably long time. This can be particularly important when dealing with an urgent issue such as that of the COVID-19 pandemic. This book presents the proceedings of two conferences: MMBD 2021 and MLIS 2021. The MMBD conference deals with two main subjects; those of Big Data and Modern Management. The MLIS conference aims to provide a platform for knowledge exchange of the most recent scientific and technological advances in the field of machine learning and intelligent systems. Both conferences were originally scheduled to be held from 8-11 November 2021, in Quanzhou, China and Xiamen, China respectively. Both conferences were ultimately held fully online on the same dates, hosted by Huaqiao University in Quanzhou and Xiamen respectively. The book is in two parts, and contains a total of 78 papers (54 from MMBD2021 and 24 from MLIS2021) selected after rigorous review from a total of some 300 submissions. The reviewers bore in mind the breadth and depth of the research topics that fall within the scope of MMBD and MLIS, and selected the 78 most promising and FAIA mainstream-relevant contributions for inclusion in this two-part volume. All the papers present original ideas or results of general significance supported by clear reasoning, compelling evidence and rigorous methods.

Handbook of Research on Artificial Intelligence in Human Resource Management

Strohmeier, Stefan
Handbook of Research on Artificial Intelligence in Human Resource Management Book PDF

Author : Strohmeier, Stefan
Publisher : Edward Elgar Publishing
Page : 416 pages
File Size : 44,6 Mb
Release : 2022-03-08
Category : Business & Economics
ISBN : 9781839107535

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Handbook of Research on Artificial Intelligence in Human Resource Management by Strohmeier, Stefan Pdf

This cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.