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Books on the topic 'Data and human knowledge learning'

Author: Grafiati

Published: 11 January 2025

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1

Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia, and SpringerLink (Online service), eds. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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2

Spiliopoulou, Myra, Lars Schmidt-Thieme, and Ruth Janning, eds. Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.

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3

Lausen, Berthold, Sabine Krolak-Schwerdt, and Matthias Böhmer, eds. Data Science, Learning by Latent Structures, and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44983-7.

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4

Chadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.

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5

Chadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.

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6

Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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7

E, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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8

Sallis, Edward. Knowledge management in education: Enhancing learning & education. London: Kogan Page, 2002.

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9

Dong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7.

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10

Dong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86514-6.

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11

Dong, Yuxiao, Dunja Mladenić, and Craig Saunders, eds. Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67667-4.

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12

Bifet, Albert, Tomas Krilavičius, Ioanna Miliou, and Slawomir Nowaczyk, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70378-2.

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13

Bifet, Albert, Tomas Krilavičius, Ioanna Miliou, and Slawomir Nowaczyk, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70381-2.

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14

Carley, Moira T. Creative learning & living: The human element. Montreal: Thomas More Institute Papers, 2005.

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15

Holzinger, Andreas, and Gabriella Pasi, eds. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39146-0.

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16

Wilkins, David C. Sociopathic knowledge bases: Correct knowledge can be harmful even given unlimited computation. Urbana, Illinois: Department of Computer Science, University of Illinois, 1989.

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17

Andrée, Tiberghien, Mandl Heinz, and NATO Advanced Research Workshop on Knowledge Acquisition in the Domain of Physics and Intelligent Learning Environments (1990 : Lyon, France), eds. Intelligent learning environments and knowledge acquisition in physics. Berlin: Springer-Verlag, 1992.

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18

Wong, Wilson. Ontology learning and knowledge discovery using the Web: Challenges and recent advances. Hershey, PA: Information Science Reference, 2011.

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19

Hamel, Lutz. Knowledge discovery with support vector machines. Hoboken, N.J: John Wiley & Sons, 2009.

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20

Tiberghien, Andrée. Intelligent Learning Environments and Knowledge Acquisition in Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.

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21

El-Nasr, Magy Seif, Alessandro Canossa, Truong-Huy D. Nguyen, and Anders Drachen. Game Data Science. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192897879.001.0001.

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This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book introduces different methods borrowing from different fields including human computer interaction, machine learning, and data science, focusing on methods and techniques used by both industry and researchers within the field of games. The book examples and case studies specifically focus on gameplay log data. The book takes a practical stance on the subject by discussing theoretical foundation, practical approaches, and delves deeply into the different techniques proposed and used through labs, examples, and comprehensive surveys of various case studies from both industry and academia. Topics range from simple approaches to more advanced ones. No prior knowledge is required. The book is developed to be self contained and can be used as a good way to introduce the reader to data science and how it is applied to the filed of games.

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22

Lamberts, Koen, and David R. Shanks, eds. Knowledge, Concepts, and Categories. The MIT Press, 1997. http://dx.doi.org/10.7551/mitpress/4071.001.0001.

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The study of mental representation is a central concern incontemporary cognitive psychology. Knowledge, Concepts, and Categories is unusual in that it presents key conclusions from across the different subfields of cognitive psychology. The study of mental representation is a central concern in contemporary cognitive psychology. Knowledge, Concepts, and Categories is unusual in that it presents key conclusions from across the different subfields of cognitive psychology. Readers will find data from many areas, including developmental psychology, formal modeling, neuropsychology, connectionism, and philosophy. The difficulty of penetrating the fundamental operations of the mind is reflected in a number of ongoing debates discussed—for example, do distinct brain systems underlie the acquisition and storage of implicit and explicit knowledge, or can the evidence be accommodated by a single-system account of knowledge representation? The book can be divided into three distinct parts. Chapters 1 through 5 offer an introduction to the field; each presents a systematic review of a significant aspect of research on concepts and categories. Chapters 6 through 9 are concerned primarily with issues related to the taxonomy of human knowledge. Finally, Chapters 10 through 12 discuss formal models of categorization and function learning. ContributorsJerome R. Busemeyer, Eunhee Byun, Nick Chater, Paul De Boeck, Edward L. Delosh, Thomas Goschke, Ulrike Hahn, James Hampton, Evan Heit, Barbara Knowlton, Koen Lamberts, Mary E. Lassaline, Mark A. McDaniel, George L. Murphy, Larissa K. Samuelson, David Shanks, Linda B. Smith, Gert Storms, Bruce W.A. Whittlesea

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23

Davis, Howard. Human Rights Law Directions. 5th ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/he/9780198871347.001.0001.

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Without assuming prior legal knowledge, books in the Directions series introduce and guide readers through key points of law and legal debate. Self-test questions and exam questions help readers to engage fully with each subject and check their understanding as they progress. Human Rights Law Directions has been written expressly to guide you through your study of human rights law, and to explain clearly and concisely the key areas of this fascinating subject. Combining academic quality with innovative learning features and online support, this is an ideal text for those studying human rights law for the first time. This fifth edition has been fully updated with key developments in human rights law, including: discussion, in so far as information allows, of proposed reform of the legal protection of human rights in the United Kingdom, post-‘Brexit’; the ECtHR case law on unlawful rendition; deportation and human rights; the impact of human rights on warfare and the condition of British troops abroad; the impact of Article 8 on abortion and assisted suicide; concerns over surveillance and communications data; the impact of human rights law on controversies over religious dress (such as the burqa ban in France); and possible infringements of rights by the legal response to Coronavirus.

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24

Huang, Su-Yun. Statistical Learning: Building Knowledge from Data. Wiley & Sons, Incorporated, John, 2014.

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25

Huang, Su-Yun. Statistical Learning: Building Knowledge from Data. Wiley & Sons, Incorporated, John, 2021.

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26

Huang, Su-Yun. Statistical Learning: Building Knowledge from Data. Wiley & Sons, Incorporated, John, 2021.

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27

Huang, Su-Yun. Statistical Learning: Building Knowledge from Data. Wiley & Sons, Incorporated, John, 2021.

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28

Nuzzo, Michele di. Data Science and Machine Learning: From Data to Knowledge. Independently Published, 2021.

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29

Nuzzo, Michele Di. Data Science and Machine Learning: From Data to Knowledge. Lulu Press, Inc., 2022.

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30

Gama, Joao. Knowledge Discovery from Data Streams. Taylor & Francis Group, 2010.

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31

Gama, Joao. Knowledge Discovery from Data Streams. Taylor & Francis Group, 2010.

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32

Gama, Joao. Knowledge Discovery from Data Streams. Taylor & Francis Group, 2010.

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33

Data Analysis Machine Learning and Knowledge Discovery. Springer International Publishing AG, 2013.

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34

Janning, Ruth, Myra Spiliopoulou, and Lars Schmidt-Thieme. Data Analysis, Machine Learning and Knowledge Discovery. Springer London, Limited, 2013.

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35

Machine Learning And Knowledge Discovery In Databases. Springer, 2011.

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36

Kannan, Ramakrishnan, Vipin Kumar, and Anuj Karpatne. Knowledge Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data. Taylor & Francis Group, 2022.

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37

Kannan, Ramakrishnan, Vipin Kumar, and Anuj Karpatne. Knowledge Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data. Taylor & Francis Group, 2022.

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38

Kannan, Ramakrishnan, Vipin Kumar, and Anuj Karpatne. Knowledge Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data. Taylor & Francis Group, 2022.

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39

Kannan, Ramakrishnan, Vipin Kumar, and Anuj Karpatne. Knowledge Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data. CRC Press LLC, 2022.

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40

Diday, E. Data Analysis, Learning Symbolic and Numeric Knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge : An. Nova Science Publishers, 1989.

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41

Maimom, Oded. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook. Springer International Publishing AG, 2023.

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42

Knowledge discovery from data streams. Boca Raton, FL: Chapman & Hall/CRC, 2010.

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43

Cafaro, Francesco, and Jessica Roberts. Data Through Movement: Designing Embodied Human-Data Interaction for Informal Learning. Morgan & Claypool Publishers, 2021.

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44

Cafaro, Francesco, and Jessica Roberts. Data Through Movement: Designing Embodied Human-Data Interaction for Informal Learning. Springer International Publishing AG, 2021.

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45

Cafaro, Francesco, and Jessica Roberts. Data Through Movement: Designing Embodied Human-Data Interaction for Informal Learning. Morgan & Claypool Publishers, 2021.

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46

Cafaro, Francesco, and Jessica Roberts. Data Through Movement: Designing Embodied Human-Data Interaction for Informal Learning. Morgan & Claypool Publishers, 2021.

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47

Rao, R. Bharat, Glenn Fung, and Romer Rosales. Knowledge-Driven Medicine: A Machine Learning Approach. Taylor & Francis Group, 2021.

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48

Lausen, Berthold, Sabine Krolak-Schwerdt, and Matthias Böhmer. Data Science, Learning by Latent Structures, and Knowledge Discovery. Springer London, Limited, 2015.

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49

Data Science, Learning by Latent Structures, and Knowledge Discovery. Springer, 2015.

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50

Crowdsourcing Geographic Knowledge. Springer, 2012.

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