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Books on the topic 'Probability learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Batanero, Carmen, Egan J. Chernoff, Joachim Engel, Hollylynne S. Lee, and Ernesto Sánchez. Research on Teaching and Learning Probability. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31625-3.

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2

DasGupta, Anirban. Probability for Statistics and Machine Learning. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3.

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3

Aggarwal, Charu C. Probability and Statistics for Machine Learning. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53282-5.

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4

Egan, J. Chernoff, Engel Joachim, Lee Hollylynne S, and Sánchez Ernesto, eds. Research on Teaching and Learning Probability. Cham: Springer, 2016.

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5

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9.

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6

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6.

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7

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3.

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8

Powell, Warren B. Optimal learning. Hoboken, New Jersey: Wiley, 2012.

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9

Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.

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10

Knez, Igor. To know what to know before knowing: Acquisition of functional rules in probabilistic ecologies. Uppsala: Uppsala University, 1992.

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11

Research Institute for Advanced Computer Science (U.S.), ed. Bayesian learning. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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12

ERIC Clearinghouse for Science, Mathematics, and Environmental Education., ed. Resources for teaching and learning about probability and statistics. [Columbus, Ohio]: ERIC Clearinghouse for Science, Mathematics and Environmental Education, 1999.

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13

author, Mak M. W., ed. Machine learning for protein subcellular localization prediction. Boston: De Gruyter, 2015.

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14

Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.

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15

DasGupta, Anirban. Probability for statistics and machine learning: Fundamentals and advanced topics. New York: Springer, 2011.

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16

Jin, Tiantian. Effect on Superficial Variability of Examples on Learning Applied Probability. [New York, N.Y.?]: [publisher not identified], 2018.

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17

Velleman, Paul F. Learning data analysis with Data desk. New York: W.H. Freeman, 1993.

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18

Lim, Chee Peng. An incremental adaptive network for on-line, supervised learning and probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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19

Gabbay, Dov M. Abductive Reasoning and Learning. Dordrecht: Springer Netherlands, 2000.

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20

Palfrey, Thomas R. Testing game-theoretic models of free riding: New evidence on probability bias and learning. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1990.

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21

I, Williams Christopher K., ed. Gaussian processes for machine learning. Cambridge, Mass: MIT Press, 2006.

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22

Rasmussen, Carl Edward. Gaussian processes for machine learning. Cambridge, MA: MIT Press, 2005.

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23

Vidyasagar, M. Learning and Generalisation: With Applications to Neural Networks. London: Springer London, 2003.

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24

1945-, Basak Subhash C., ed. Statistical and machine learning approaches for network analysis. Hoboken, N.J: Wiley, 2012.

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25

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2016.

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26

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2020.

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27

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer, 2019.

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28

Unpingco, José. Python for Probability, Statistics, and Machine Learning. Springer London, Limited, 2016.

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29

Python for Probability, Statistics, and Machine Learning. Springer International Publishing AG, 2023.

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30

Python for Probability, Statistics, and Machine Learning. Springer International Publishing AG, 2022.

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31

Peck, Roxy, and Chris Olsen. Statistics: Learning from Data. Brooks/Cole, 2013.

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32

Peck, Roxy. Statistics: Learning from Data. Brooks/Cole, 2017.

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33

Peck, Roxy. Statistics: Learning from Data. Brooks/Cole, 2013.

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34

Peck, Roxy. Statistics: Learning from Data. Cengage Learning, 2023.

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35

Probability and Statistics for Machine Learning: A Textbook. Springer, 2024.

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36

Schrope, Byron. Probability and Its Concepts: Give Your Business an Edge by Learning More about Probability. Independently Published, 2022.

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37

Knox, Steven W. Machine Learning: a Concise Introduction (Wiley Series in Probability and Statistics). Wiley, 2018.

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38

Duerr, Oliver, Beate Sick, and Elvis Murina. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability. Manning Publications, 2020.

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39

Batanero, Carmen, and Egan J. Chernoff. Teaching and Learning Stochastics: Advances in Probability Education Research. Springer, 2018.

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40

Batanero, Carmen, and Egan J. Chernoff. Teaching and Learning Stochastics: Advances in Probability Education Research. Springer, 2019.

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41

Tomar, Simit. Probability and Statistics for Data Science and Machine Learning. Independently Published, 2020.

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42

Duerr, Oliver, and Beate Sick. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability. Manning Publications Co. LLC, 2020.

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43

Jones, Graham A. Exploring Probability in School: Challenges for Teaching and Learning. Springer, 2010.

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44

Jones, Graham A. Exploring Probability in School: Challenges for Teaching and Learning. Springer, 2005.

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45

Adams, Christopher P. Learning Microeconometrics with R. Taylor & Francis Group, 2020.

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46

Learning Microeconometrics with R. Taylor & Francis Group, 2020.

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47

DasGupta, Anirban. Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics. Springer, 2013.

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48

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

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49

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

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50

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

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