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.