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Author(s): Madhav Kapoor1

Email(s): 1kapoormadhav2005@gmail.com

Address:

    St. George International School, C. de los Padres Dominicos, 1, 28050 Madrid

Published In:   Volume - 3,      Issue - 2,     Year - 2023

DOI: 10.55878/SES2022-2-1-5  

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ABSTRACT:
Artificial intelligence (AI) and machine learning (ML) have transformed numerous domains by enabling systems to learn from data and make intelligent decisions. Within the field of ML, probabilistic machine learning has gained significant attention due to its ability to capture uncertainty and provide predictions based on probabilities. This essay explores the concept of probabilistic machine learning and its applications in AI. We delve into the fundamentals of machine learning, discuss probabilistic modeling, explore various probabilistic machine learning techniques, highlight the advantages of probabilistic approaches, and examine real-world applications. Additionally, we address the challenges associated with probabilistic machine learning and discuss future directions in this exciting field.

Cite this article:
Madhav Kapoor * (2023), Probabilistic Machine Learning and Artificial Intelligence, Spectrum of Emerging Sciences, 3 (2), pp 29-36DOI: https://doi.org/10.55878/SES2022-2-1-5


References:

   [1]           Frauendorf, José Luiz, and Érika Almeida de Souza. "Artificial Intelligence (AI) and Machine Learning (ML)." The Architectural and Technological Revolution of 5G. Cham: Springer International Publishing, 2022. 195-204.

   [2]           Smith, Lewis, and Yarin Gal. "Understanding measures of uncertainty for adversarial example detection." arXiv preprint arXiv:1803.08533 (2018).

   [3]           Murphy, Kevin P. Probabilistic machine learning: an introduction. MIT press, 2022.

   [4]           Ghahramani, Zoubin. "Probabilistic machine learning and artificial intelligence." Nature 521.7553 (2015): 452-459.

   [5]           Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.

   [6]           Tamara Broderick et al. ,Toward a taxonomy of trust forprobabilistic machine learning.Sci. Adv.9,eabn3999(2023).DOI:10.1126/sciadv.abn3999.

   [7]           Weitzman, Martin L., et al. "Advancements and Challenges in Machine Learning: A Comprehensive Review." AC Investment Research Journal 220.44 (2023).

   [8]           Cunningham, Pádraig, Matthieu Cord, and Sarah Jane Delany. "Supervised learning." Machine learning techniques for multimedia: case studies on organization and retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. 21-49.

   [9]           Caruana, Rich, and Alexandru Niculescu-Mizil. "An empirical comparison of supervised learning algorithms." Proceedings of the 23rd international conference on Machine learning. 2006.

 [10]         Hearst, Marti A., et al. "Support vector machines." IEEE Intelligent Systems and their applications 13.4 (1998): 18-28.

 [11]         Steinwart, Ingo, and Andreas Christmann. Support vector machines. Springer Science & Business Media, 2008.

 [12]         Barlow, Horace B. "Unsupervised learning." Neural computation 1.3 (1989): 295-311.

 [13]         Hastie, Trevor, et al. "Unsupervised learning." The elements of statistical learning: Data mining, inference, and prediction (2009): 485-585.

 [14]         Hartigan, John A., and Manchek A. Wong. "Algorithm AS 136: A k-means clustering algorithm." Journal of the royal statistical society. series c (applied statistics) 28.1 (1979): 100-108.

 [15]         Belkina, Anna C., et al. "Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets." Nature communications 10.1 (2019): 5415.

 [16]         Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial intelligence research 4 (1996): 237-285.

 [17]         Wiering, Marco A., and Martijn Van Otterlo. "Reinforcement learning." Adaptation, learning, and optimization 12.3 (2012): 729.

 [18]         LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.

 [19]         Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.

 [20]         Chung, Kai Lai. A course in probability theory. Academic press, 2001.

 [21]         Box, George EP, and George C. Tiao. Bayesian inference in statistical analysis. John Wiley & Sons, 2011.

 [22]         Seeger, Matthias. "Gaussian processes for machine learning." International journal of neural systems 14.02 (2004): 69-106.

 [23]         Dymarski, Przemyslaw, ed. Hidden Markov models: theory and applications. BoD–Books on Demand, 2011.

 [24]         Chib, Siddhartha. "Markov chain Monte Carlo methods: computation and inference." Handbook of econometrics 5 (2001): 3569-3649.

 [25]         Huggins, Jonathan, et al. "Validated variational inference via practical posterior error bounds." International Conference on Artificial Intelligence and Statistics. PMLR, 2020.

 [26]         Gordon, Andrew D., et al. "Probabilistic programming." Future of Software Engineering Proceedings. 2014. 167-181.

 [27]         Chen, Irene Y., et al. "Probabilistic machine learning for healthcare." Annual review of biomedical data science 4 (2021): 393-415.

 [28]         Aziz, Saqib, et al. "Machine learning in finance: A topic modeling approach." European Financial Management 28.3 (2022): 744-770.

 [29]         Dong, Yi, Xingyu Zhao, and Xiaowei Huang. "Dependability analysis of deep reinforcement learning based robotics and autonomous systems." (2021).

 [30]         Khan, Wahab, et al. "A survey on the state-of-the-art machine learning models in the context of NLP." Kuwait journal of Science 43.4 (2016).

 [31]         Greenspan, Hayit, Jacob Goldberger, and Arnaldo Mayer. "Probabilistic space-time video modeling via piecewise GMM." IEEE Transactions on pattern analysis and machine intelligence 26.3 (2004): 384-396.

 [32]         Papacharalampous, Georgia, and Hristos Tyralis. "A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting." Frontiers in Water 4 (2022): 961954.

 [33]         Ho, Joel, Nick Pepper, and Tim Dodwell. "Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling." arXiv preprint arXiv:2301.09443 (2023).

 [34]         Broderick, Tamara, et al. "Toward a taxonomy of trust for probabilistic machine learning." Science Advances 9.7 (2023): eabn3999.

 [35]         Ahmad, Tanveer, et al. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm." Renewable and Sustainable Energy Reviews 160 (2022): 112128.

 [36]         Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

 

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DOI: 10.55878/SES2022-2-1-5         Access: Open Access Read More