Relevant bibliographies by topics / The machine learning (Machine Learning)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'The machine learning (Machine Learning)'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "The machine learning (Machine Learning)"

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M. Brandao, Iago, and Cesar da Costa. "FAULT DIAGNOSIS OF ROTARY MACHINES USING MACHINE LEARNING." Eletrônica de Potência 27, no. 03 (2022): 1–8. http://dx.doi.org/10.18618/rep.2022.3.0013.

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Naeini, Ehsan Zabihi, and Kenton Prindle. "Machine learning and learning from machines." Leading Edge 37, no. 12 (2018): 886–93. http://dx.doi.org/10.1190/tle37120886.1.

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Çelik, Rumeysa Hilal, Hacı Aslan Onur İşcil, Ecem Bulut, and Saliha Ece Acuner. "Learning molecular machines by machine learning." Eurasian Journal of Science Engineering and Technology 6, no. 2 (2025): 100–120. https://doi.org/10.55696/ejset.1620495.

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Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (
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Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed sta
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Trott, David. "Deceiving Machines: Sabotaging Machine Learning." CHANCE 33, no. 2 (2020): 20–24. http://dx.doi.org/10.1080/09332480.2020.1754067.

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Siddique, Shumaila. "Machine Learning and Cryptography." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 2540–45. http://dx.doi.org/10.5373/jardcs/v12sp7/20202387.

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Charpentier, Arthur, Emmanuel Flachaire, and Antoine Ly. "Econometrics and Machine Learning." Economie et Statistique / Economics and Statistics, no. 505d (April 11, 2019): 147–69. http://dx.doi.org/10.24187/ecostat.2018.505d.1970.

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Mor, Laksanya. "Introduction to Machine Learning." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1522–25. http://dx.doi.org/10.21275/sr22328110600.

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Lewis, Ted G., and Peter J. Denning. "Learning machine learning." Communications of the ACM 61, no. 12 (2018): 24–27. http://dx.doi.org/10.1145/3286868.

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Rasi, Mr Ajmal, Dr Rajasimha A. Makram, and Ms Shilpa Das. "Topic Detection using Machine Learning." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 1433–36. http://dx.doi.org/10.31142/ijtsrd14272.

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Dissertations / Theses on the topic "The machine learning (Machine Learning)"

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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.

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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.<br>Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små start
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Dinakar, Karthik. "Lensing Machines : representing perspective in machine learning." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.

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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.<br>Cataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.<br>Includes bibliographical references (pages 167-172).<br>Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, al
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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.

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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this s
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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version o
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Collazo, Santiago Bryan Omar. "Machine learning blocks." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references.<br>This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually a
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Shukla, Ritesh. "Machine learning ecosystem : implications for business strategy centered on machine learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/107342.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 48-50).<br>As interest for adopting machine learning as a core component of a business strategy increases, business owners face the challenge of integrating an uncertain and rapidly evolving technology into their organization, and depending on this for the success of their strategy. The field of Machine learning has a r
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Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.

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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and
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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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Kent, W. F. "Machine learning for parameter identification of electric induction machines." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.

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This thesis is concerned with the application of simulated evolution (SE) to the steady-state parameter identification problem of a simulated and real 3-phase induction machine, over the no-load direct-on-line start period. In the case of the simulated 3-phase induction machine, the Kron's two-axis dynamic mathematical model was used to generate the real and simulated system responses where the induction machine parameters remain constant over the entire range of slip. The model was used in the actual value as well as the per-unit system, and the parameters were estimated using both the geneti
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Menke, Joshua E. "Improving machine learning through oracle learning /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1726.pdf.

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Books on the topic "The machine learning (Machine Learning)"

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Zhou, Zhi-Hua. Machine Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.

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Jung, Alexander. Machine Learning. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.

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Mitchell, Tom M., Jaime G. Carbonell, and Ryszard S. Michalski. Machine Learning. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.

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Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.

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Bell, Jason. Machine Learning. John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.

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Huang, Kaizhu, Haiqin Yang, Irwin King, and Michael Lyu. Machine Learning. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.

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Jebara, Tony. Machine Learning. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.

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Hilbert, Sven, Elisabeth Kraus, and Alfred Lindl. Machine Learning. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-43649-0.

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Vorobeychik, Yevgeniy, and Murat Kantarcioglu. Adversarial Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01580-9.

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Chen, Zhiyuan, and Bing Liu. Lifelong Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01581-6.

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Book chapters on the topic "The machine learning (Machine Learning)"

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Hassan, Shabbir. "Machine learning vs. neutrosophic machine learning." In Soft Computing and Machine Learning. CRC Press, 2025. https://doi.org/10.1201/9781003606055-5.

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Wehenkel, Louis A. "Machine Learning." In Automatic Learning Techniques in Power Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9_2.

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Dinsmore, Thomas W. "Machine Learning." In Disruptive Analytics. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1311-7_8.

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Yao, Xin, and Yong Liu. "Machine Learning." In Search Methodologies. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_17.

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Ben-Ari, Mordechai, and Francesco Mondada. "Machine Learning." In Elements of Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62533-1_14.

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Kwok, James T., Zhi-Hua Zhou, and Lei Xu. "Machine Learning." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_29.

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Cobia, Derin. "Machine Learning." In Encyclopedia of Clinical Neuropsychology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_9058.

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Zielesny, Achim. "Machine Learning." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21280-2_4.

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Conference papers on the topic "The machine learning (Machine Learning)"

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Kozhenkov, A., E. Z. Naeini, and K. Prindle. "Machine Learning and Learning from Machines." In Progress’19. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201953052.

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Chaudhuri, Arjun, Jonti Talukdar, and Krishnendu Chakrabarty. "Machine Learning for Testing Machine-Learning Hardware." In ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design. ACM, 2022. http://dx.doi.org/10.1145/3508352.3561121.

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"Machine learning." In 2015 International Symposium on Advanced Computing and Communication (ISACC). IEEE, 2015. http://dx.doi.org/10.1109/isacc.2015.7377313.

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Mitrofanova, A. S., and G. V. Komlev. "Machine learning." In ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ. НИЦ «Л-Журнал», 2018. http://dx.doi.org/10.18411/lj-11-2018-180.

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"Machine Learning." In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787334.

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Young, Ramsey, and Jonathan Ringenberg. "Machine Learning." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. ACM, 2019. http://dx.doi.org/10.1145/3287324.3293806.

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Mohammed, Hadi, Ibrahim A. Hameed, and Razak Seidu. "Machine learning." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3208235.

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"Machine Learning." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854395.

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Jordan, Michael I. "Machine learning." In TURC 2018: ACM Turing Celebration Conference - China. ACM, 2018. http://dx.doi.org/10.1145/3210713.3210718.

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Han, DongYeob. "Crack detection of UAV concrete surface images." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2525174.

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Reports on the topic "The machine learning (Machine Learning)"

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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada223732.

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Kagie, Matthew J., and Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1561828.

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Lin, Youzuo, Shihang Feng, and Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1876777.

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Vassilev, Apostol. Adversarial Machine Learning:. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.

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Kelly, Bryan, and Dacheng Xiu. Financial Machine Learning. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31502.

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Caplin, Andrew, Daniel Martin, and Philip Marx. Modeling Machine Learning. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30600.

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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview
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Vassilev, Apostol. Adversarial Machine Learning:. National Institute of Standards and Technology, 2025. https://doi.org/10.6028/nist.ai.100-2e2025.

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