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Bibliographies thématiques / MACHINE LEARNING TOOL / Livres

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Livres sur le sujet « MACHINE LEARNING TOOL »

Auteur : Grafiati

Publié le 26 octobre 2023

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1

Houser, David Allan. Machine learning as a quality improvement tool. Ottawa : National Library of Canada, 1996.

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2

Building intelligent agents : An apprenticeship multistrategy learning theory, methodology, tool and case studies. San Diego : Academic Press, 1998.

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3

Learning computer numerical control. Albany, NY : Delmar Publishers, 1992.

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4

Cost-sensitive machine learning. Boca Raton, FL : CRC Press, 2012.

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5

Khosrowpour, Mehdi, et Information Resources Management Association. Machine learning : Concepts, methodologies, tools and applications. Hershey, PA : Information Science Reference, 2012.

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6

Eibe, Frank, et Hall Mark A, dir. Data mining : Practical machine learning tools and techniques. 3^e éd. Burlington, MA : Morgan Kaufmann, 2011.

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7

Machine learning : A probabilistic perspective. Cambridge, MA : MIT Press, 2012.

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8

Castiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.

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9

Pardalos, Panos M., Stamatina Th Rassia et Arsenios Tsokas, dir. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.

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10

Witten, I. H. Data mining : Practical machine learning tools and techniques with Java implementations. San Francisco, Calif : Morgan Kaufmann, 2000.

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11

Srinivasa, K. G., G. M. Siddesh et S. R. Manisekhar, dir. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5.

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12

National Institute of Standards and Technology (U.S.), dir. Manufacturing technology learning modules : Sharing resources for school outreach. Gaithersburg, MD : U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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13

Topolsky, Nikolay, et Valeriy Vilisov. Methods, models and algorithms in security systems : machine learning, robotics, insurance, risks, control. ru : Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02072-2.

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The monograph examines topical issues of decision support and management in safety systems for fire and emergency situations through the use of innovative approaches and tools for operations research, artificial intelligence, robotics and management methods in organizational systems. The monograph is intended for faculty, researchers, graduate students (adjuncts) and doctoral students, as well as for undergraduates, students and listeners of educational organizations, all those who are interested in the problems of decision support and management in security systems.

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14

Statistical learning and data science. Boca Raton : CRC Press, 2012.

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15

Fiebrink, Rebecca A., et Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Sous la direction de Roger T. Dean et Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.

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16

Building Intelligent Agents : An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case Studies. Elsevier Science & Technology Books, 1998.

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17

Machine Learning with Python : The Definitive Tool to Improve Your Python Programming and Deep Learning to Take You to the Next Level of Coding and Algorithms Optimization. Independently Published, 2021.

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18

Wright, Ivy. Machine Learning : Concepts, Tools and Techniques. States Academic Press, 2022.

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19

Wall, Eric. Python Crash Course : A Beginner's Guide to Master the Basics of Python and Data Science. Learn Coding with This Machine Learning Tool. Discover the Endless Possibilities of Computers and Codes. Independently Published, 2020.

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20

Python Crash Course : A Beginner's Guide to Master the Basics of Python and Data Science. Learn Coding with This Machine Learning Tool. Discover the Endless Possibilities of Computers and Codes. Independently Published, 2020.

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21

Learn Python : This Book Includes : Crash Course and Coding. a Guide to Master Python, Data Science and Analysis. Advanced Methods to Learn How to Create Codes with This Machine Learning Tool. Independently Published, 2020.

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22

Yu, Shipeng, Balaji Krishnapuram et R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2019.

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23

Yu, Shipeng, Balaji Krishnapuram et R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2011.

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24

Yu, Shipeng, Balaji Krishnapuram et R. Bharat Rao. Cost-Sensitive Machine Learning. Taylor & Francis Group, 2011.

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25

Janke, Michael. Learning Computer Numerical Control : Instructor's Guide. Natl Tooling & Machining Assn, 1996.

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26

Irma. Machine Learning : Concepts, Methodologies, Tools and Applications. IGI Global, 2011.

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27

IRMA. Machine Learning : Concepts, Methodologies, Tools and Applications. Information Science Reference, 2011.

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28

Choi, Eunsoo, et Minsoo Kang. Machine Learning : Concepts, Tools and Data Visualization. World Scientific Publishing Co Pte Ltd, 2021.

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29

IRMA. Machine Learning : Concepts, Methodologies, Tools and Applications. Information Science Reference, 2011.

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30

Choi, Eunsoo, et Minsoo Kang. Machine Learning : Concepts, Tools and Data Visualization. World Scientific Publishing Co Pte Ltd, 2021.

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31

Mather, Bob. Machine Learning in Python : Hands on Machine Learning with Python Tools, Concepts and Techniques. Independently Published, 2018.

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32

Data Mining : Practical Machine Learning Tools and Techniques. Elsevier, 2011. http://dx.doi.org/10.1016/c2009-0-19715-5.

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33

Data mining : practical machine learning tools and techniques. Morgan Kaufmann, 2017.

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34

Witten, Ian H., Eibe Frank, Hall Mark A et Christopher Pal. Data Mining : Practical Machine Learning Tools and Techniques. Elsevier Science & Technology Books, 2016.

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35

Data Mining : Practical Machine Learning Tools and Techniques. Elsevier Science & Technology Books, 2011.

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36

Eddaly, Mansour, Patrick Siarry et Bassem Jarboui. Metaheuristics for Machine Learning : New Advances and Tools. Springer, 2022.

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37

Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

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38

Murphy, Kevin P. Machine Learning : A Probabilistic Perspective. MIT Press, 2012.

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39

Murphy, Kevin P. Machine Learning : A Probabilistic Perspective. MIT Press, 2012.

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40

R. Larsen, Kai, et Daniel S. Becker. Automated Machine Learning for Business. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190941659.001.0001.

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In Automated Machine Learning for Business, we teach the machine learning process using a new development in data science: automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high school curriculum.

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41

Eldar, Yonina C., Andrea Goldsmith, Deniz Gündüz et H. Vincent Poor, dir. Machine Learning and Wireless Communications. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108966559.

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How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.

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42

Castiello, Maria Elena. Computational and Machine Learning Tools for Archeological Site Modeling. Springer International Publishing AG, 2021.

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43

Computational and Machine Learning Tools for Archaeological Site Modeling. Springer International Publishing AG, 2023.

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44

Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Incorporated, 2022.

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45

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.

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46

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.

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47

Etaati, Leila. Machine Learning with Microsoft Technologies : Selecting the Right Architecture and Tools for Your Project. Apress, 2019.

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48

Basuchoudhary, Atin, James T. Bang et Tinni Sen. Machine-learning Techniques in Economics : New Tools for Predicting Economic Growth. Springer, 2017.

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49

Hands-On Machine Learning with Scikit-Learn and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2017.

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50

Machine Learning and Big Data : Concepts, Algorithms, Tools and Applications. Wiley & Sons, Limited, John, 2020.

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