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Books on the topic 'Artificial intelligence and machine learning'

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1

BOOKS, Editors of TIME-LIFE. Artificial intelligence. Edited by Time-Life Books. Alexandra, Va: Time-Life Books, 1991.

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2

Joshi, Ameet V. Machine Learning and Artificial Intelligence. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26622-6.

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3

Bogaerts, Bart, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, and Gilles Louppe, eds. Artificial Intelligence and Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65154-1.

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4

Baratchi, Mitra, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, and Frank W. Takes, eds. Artificial Intelligence and Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76640-5.

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5

Furukawa, Kōichi. Machine intelligence 14: Applied machine intelligence. Oxford: Clarendon Press, 2002.

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6

On machine intelligence. 2nd ed. Chichester: E. Horwood, 1986.

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7

Artificial Intelligence and Intelligent Systems. Oxford: Oxford University Press, 2005.

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8

Ramanna, Sheela. Emerging Paradigms in Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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9

Artificial intelligence: How machines think. New York, N.Y: Baen Books, 1985.

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10

Saxena, Ankur, and Shivani Chandra. Artificial Intelligence and Machine Learning in Healthcare. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0811-7.

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11

Choudhary, Ankur, Arun Prakash Agrawal, Rajasvaran Logeswaran, and Bhuvan Unhelkar, eds. Applications of Artificial Intelligence and Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3067-5.

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12

Intelligence, International Conference of the Catalan Association for Artificial. Artificial intelligence research and development. Amsterdam: IOS Press, 2006.

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13

Artificial intelligence: Opposing viewpoints. San Diego, Calif: Greenhaven Press, 1990.

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14

Utgoff, Paul E. Machine Learning of Inductive Bias. Boston, MA: Springer US, 1986.

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15

Machine learning: A theoretical approach. San Mateo, CA: M. Kaufmann, 1991.

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16

Michalski, Ryszard S. Multistrategy Learning: A Special Issue of MACHINE LEARNING. Boston, MA: Springer US, 1993.

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17

Holzinger, Andreas, Randy Goebel, Michael Mengel, and Heimo Müller, eds. Artificial Intelligence and Machine Learning for Digital Pathology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50402-1.

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18

Al-Turjman, Fadi, ed. Artificial Intelligence and Machine Learning for COVID-19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60188-1.

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19

Mohan, Neeraj, Ruchi Singla, Priyanka Kaushal, and Seifedine Kadry. Artificial Intelligence, Machine Learning, and Data Science Technologies. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003153405.

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20

Panda, Sandeep Kumar, Vaibhav Mishra, R. Balamurali, and Ahmed A. Elngar. Artificial Intelligence and Machine Learning in Business Management. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003125129.

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21

Kodratoff, Yves. Introduction to machine learning. San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1988.

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22

E, Nicholson Ann, ed. Bayesian artificial intelligence. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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23

Cipolla, Roberto. Machine Learning for Computer Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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24

Segre, Alberto Maria. Machine Learning of Robot Assembly Plans. Boston, MA: Springer US, 1988.

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25

Brazdil, Pavel B. Machine Learning, Meta-Reasoning and Logics. Boston, MA: Springer US, 1989.

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26

Bolc, Leonard. Computational Models of Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987.

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27

G, Carbonell Jaime, and Michalski Ryszard S, eds. Machine Learning: A Guide to Current Research. Boston, MA: Springer US, 1986.

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28

J, Nilsson Nils. The mathematical foundationsof learning machines. San Mateo, Calif: Morgan Kaufmann, 1990.

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29

Weiß, Gerhard, ed. Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62934-3.

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30

McDermott, Gabriel. The hierachical learning system: A framework for integrating analytic and synthetic learning strategies. Dublin: University College Dublin, 1997.

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31

(Editor), K. Furukawa, D. Michie (Editor), and S. Muggleton (Editor), eds. Machine Intelligence 13: Machine Intelligence and Inductive Learning (Machine Intelligence). Oxford University Press, USA, 1994.

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32

Langley, Pat, and Jaime Carbonell. Machine Learning. AAAI Press, 1988.

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33

Books, Time-Life, ed. Artificial intelligence. Alexandria, Va: Time-Life Books, 1986.

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34

Tallón-Ballesteros, Antonio J., and Chi-Hua Chen, eds. Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia332.

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35

Joshi, Ameet V. Machine Learning and Artificial Intelligence. Springer, 2019.

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36

Kōichi, Furukawa, Michie Donald, Muggleton Stephen, and Machine Intelligence Workshop (14th : 1993 : Hitachi Research Laboratory), eds. Machine intelligence 14: Applied machine intelligence. Oxford: Clarendon Press, 1996.

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37

(Editor), K. Furukawa, D. Michie (Editor), and S. Muggleton (Editor), eds. Machine Intelligence 14: Applied Machine Intelligence. Oxford University Press, USA, 1996.

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38

D, Sleeman, and Bernsen Niels Ole, eds. Artificial intelligence. Hove, UK: L. Erlbaum Associates, 1992.

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39

Koichi, Furukawa, Michie Donald, and Muggleton S, eds. Machine intelligence. Oxford: Oxford University Press, 1995.

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40

Goyal, Dinesh, S. Balamurugan, Nedunchezhian Raju, M. Rajalakshmi, and Ahmed Elngar. Empowering Artificial Intelligence Through Machine Learning. Apple Academic Press, Incorporated, 2020.

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41

Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann Pub, 1990.

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42

Goyal, Dinesh, S. Balamurugan, Nedunchezhian Raju, M. Rajalakshmi, and Ahmed Elngar. Empowering Artificial Intelligence Through Machine Learning. Apple Academic Press, Incorporated, 2020.

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43

ANSARI, Hasanraza. Artificial Intelligence with Python: Machine Learning. Independently Published, 2020.

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44

Goyal, Dinesh, S. Balamurugan, Nedunchezhian Raju, M. Rajalakshmi, and Ahmed Elngar. Empowering Artificial Intelligence Through Machine Learning. Apple Academic Press, Incorporated, 2020.

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45

Braegelmann, Tom, and Markus Kaulartz, eds. Rechtshandbuch Artificial Intelligence und Machine Learning. Verlag C.H.BECK oHG, 2020. http://dx.doi.org/10.17104/9783406761928.

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46

Goyal, Dinesh, S. Balamurugan, Nedunchezhian Raju, M. Rajalakshmi, and Ahmed Elngar. Empowering Artificial Intelligence Through Machine Learning. Apple Academic Press, Incorporated, 2020.

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47

Carbonell, Jaime G., Ryszard S. Michalski, and Tom M. Mitchell. Machine Learning: An Artificial Intelligence Approach, Volume II (Machine Learning). Morgan Kaufmann, 1986.

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48

Machine Learning: An Artificial Intelligence Approach, Volume III (Machine Learning). Morgan Kaufmann, 1990.

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49

Proudfoot, Diane, and B. Jack Copeland. Artificial Intelligence. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

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Abstract:
In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article inMind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.
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50

Jain, Lakhmi C., Robert J. Howlett, and Sheela Ramanna. Emerging Paradigms in Machine Learning. Springer, 2014.

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