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Relevant bibliographies by topics / Bayesian Machine Learning (BML) / Books

Books on the topic 'Bayesian Machine Learning (BML)'

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Published: 6 September 2023

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1

Barber, David. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press, 2011.

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2

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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3

Learning Bayesian networks. Harlow: Prentice Hall, 2003.

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4

Neapolitan, Richard E. Learning Bayesian networks. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.

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5

Neal, Radford M. Bayesian learning for neural networks. New York: Springer, 1996.

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6

Neal, Radford M. Bayesian learning for neural networks. Toronto: University of Toronto, Dept. of Computer Science, 1995.

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7

Hemachandran, K., Shubham Tayal, Preetha Mary George, Parveen Singla, and Utku Kose. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003164265.

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8

Cheng, Lei, Zhongtao Chen, and Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22438-6.

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9

MACKAY, DAVID J. C. Information Theory, Inference & Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003.

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10

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

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11

Jensen, Finn V. An introduction to Bayesian networks. London: UCL Press, 1996.

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12

Jensen, Finn V. An introduction to Bayesian networks. New York: Springer, 1996.

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13

Approximation methods for efficient learning of Bayesian networks. Amsterdam: IOS Press, 2008.

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14

Mikhail, Kanevski, ed. Advanced mapping of environmental data: Geostatistics, machine learning, and Bayesian maximum entropy. London: ISTE, Ltd., 2008.

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15

Fagan, Francois Johannes. Advances in Bayesian inference and stable optimization for large-scale machine learning problems. [New York, N.Y.?]: [publisher not identified], 2019.

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16

Learning Bayesian models with R: Become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems. Birmingham, UK: Packt Publishing, 2015.

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17

E, Nicholson Ann, ed. Bayesian artificial intelligence. 2nd ed. Boca Raton, FL: CRC Press, 2011.

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18

Bayesian networks and decision graphs. New York: Springer, 2001.

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19

Tulupʹev, A. L. Baĭesovskie seti: Logiko-veroi︠a︡tnostnyĭ podkhod. Sankt-Peterburg: "Nauka", 2006.

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20

Dechter, Rina. Reasoning with probabilistic and deterministic graphical models: Exact algorithms. San Rafael, California]: Morgan & Claypool Publishers, 2013.

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21

Barber, David. Bayesian Reasoning and Machine Learning. University of Cambridge ESOL Examinations, 2016.

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22

Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

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23

Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

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24

Barber, David. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

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25

Barber, David. Bayesian Reasoning and Machine Learning Paperback. Cambridge University Press, 2014.

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26

Theodoridis, Sergios. Machine Learning. Elsevier Science & Technology Books, 2020.

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27

Ghavamzadeh, Mohammad, Shie Mannor, Joelle Pineau, and Aviv Tamar. Bayesian Reinforcement Learning: A Survey. Now Publishers, 2015.

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28

Machine Learning: A Bayesian and Optimization Perspective. Academic Press, 2015.

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29

Theodoridis, Sergios. Machine Learning: A Bayesian and Optimization Perspective. Elsevier Science & Technology Books, 2015.

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30

Neal, Radford M. Bayesian Learning for Neural Networks. Springer London, Limited, 2012.

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31

Kose, Utku, Shubham Tayal, Parveen Singla, Hemachandran K, and Preetha Mary George. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. Taylor & Francis Group, 2022.

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32

Kose, Utku, Shubham Tayal, Parveen Singla, Hemachandran K, and Preetha Mary George. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. Taylor & Francis Group, 2022.

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33

Kose, Utku, Shubham Tayal, Parveen Singla, Hemachandran K, and Preetha Mary George. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. Taylor & Francis Group, 2022.

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34

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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35

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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36

Zhou, Xuefeng. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Nature, 2020.

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37

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2010.

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38

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2010.

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39

Gámez, José A., Serafín Moral, and Antonio Salmerón. Advances in Bayesian networks. 2010.

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40

Nielsen, Thomas Dyhre, and Finn VERNER JENSEN. Bayesian Networks and Decision Graphs. Springer, 2007.

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41

Nielsen, Thomas Dyhre, and Finn VERNER JENSEN. Bayesian Networks and Decision Graphs. Springer New York, 2013.

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42

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms. Morgan & Claypool Publishers, 2019.

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43

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms. Morgan & Claypool Publishers, 2019.

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44

Brachman, Ronald J., Rina Dechter, Francesca Rossi, and Peter Stone. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition. Morgan & Claypool Publishers, 2019.

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45

Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Abstract:

Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

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46

Vidales, A. MACHINE LEARNING with MATLAB: GAUSSIAN PROCESS REGRESSION, ANALYSIS of VARIANCE and BAYESIAN OPTIMIZATION. Independently Published, 2019.

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47

Takikawa, Masami. Representations and algorithms for efficient inference in Bayesian networks. 1998.

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48

Korb, Kevin B., and Ann E. Nicholson. Bayesian Artificial Intelligence. Taylor & Francis Group, 2003.

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49

Bayesian Artificial Intelligence. Taylor & Francis Group, 2023.

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50

Cheng, Lei, Zhongtao Chen, and Yik-Chung Wu. Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms and Applications. Springer International Publishing AG, 2023.

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