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.scikit-yb.org . Accessed: 2020-11-30. [34] Fair crowd work: Shedding light on the real work of crowd-, platform-, and app-based work. http://faircrowd.work/platform-reviews , 2018. Accessed: 2020-11-30. [35] Amershi S., Begel A., Bird C., DeLine R., Gall H., Kamar E., Nagappan N., Nushi B., and Zimmermann T. Software engineering for machine learning: A case study. In 41st IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice , pages 291–300. IEEE, 2019. [36] Badene S., Thompson K., Lorré J.-P., and Asher N. Weak supervision for learning discourse

://cloud.google.com/automl/docs/ [4] TUGGENER, L., AMIRIAN, M., ROMBACH, K., LÖRWALD, S., VARLET, A., WESTERMANN, CH., STADELMANN, T. 2019. Automated Machine Learning in Practice: State of the Art and Recent Results. In: 6th Swiss Conference on Data Science (SDS) : Bern, Switzerland, pp. 31-36. ISBN 978-1-7281-3105-4. [5] THORNTON, C., HUTTER, F., HOOS, H. H., LEYTON-BROWN, K. 2013. Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. In: 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ‘13 : Chicago, Illinois, USA, pp

References [1] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov. Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ’16), Vienna, Austria. pp 308-318. [2] L. J. M. Aslett, P. M. Esperança, C. C. Holmes. Encrypted statistical machine learning: new privacy preserving methods. CoRR abs/1508.06845 (2015). [3] L. J. M. Aslett, P. M. Esperanca, C. Holmes. A review of homomorphic encryption and software tools for encrypted statistical machine learning. Tech. rep., University of

References [1] KDD Cup 1999 Data http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html [2] Ősz R., Holik I.: P edagógiai kutatásmódszertan , Óbudai Egyetem, 2015. [3] Géron A.: Hands-on Machine Learning with Scikit- Learn, Keras, and TensorFlow. Concepts, Tools, and Techniques to Build Intelligent Systems , O’Reilly, 2019. ISBN 978-1-492-03264-9 [4] NSL-KDD dataset, https://www.unb.ca/cic/datasets/nsl.html [5] Mitchell T. M.: Machine learning. McGraw-Hill Science/Engineering/Math, 1997.

Lithology Prediction from Well Logging Using Kernel Density Estimation . Norwegian University of Science and Technology, 2016. [19] R. Gelfort, “On Classification of Logging Data,” Dissertation, 2005. [20] E. Howat, S. Mishra, J. Schuetter, B. Grove and A. Haagsma, “Identification of Vuggy Zones in Carbonate Reservoirs from Wireline Logs Using Machine Learning Techniques,” American Association of Petroleum Geologists Eastern Regional Meeting , 2015. [21] B. V. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques . IEEE Computer Society Press

R eferences [1] T. V. Ark, “Ask About AI - The Future of Work and Learning,” Getting Smart Staff, 2017. [2] E. Koblentz, “How to implement AI and machine learning,” TechRepublic, 2016. [3] Z. Mohamed and P. Bodger, “Forecasting electricity consumption in New Zealand using economic and demographic variables,” Energy, vol. 30, no. 10, pp. 1833–1843, 2005. https://doi.org/10.1016/j.energy.2004.08.012 [4] M. Yang and X. Yu, “China’s rural electricity market – a quantitative analysis,” Energy, vol. 29, no. 7, pp. 961–977, 2004. https://doi.org/10.1016/j

. 2017. [32] I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio. Binarized neural networks. In NIPS , 2016. [33] Y. Ishai, J. Kilian, K. Nissim, and E. Petrank. Extending Oblivious Transfers Efficiently. In CRYPTO , 2003. [34] Y. Ishai, R. Kumaresan, E. Kushilevitz, and A. Paskin-Cherniavsky. Secure computation with minimal interaction, revisited. In CRYPTO , 2015. [35] J.So, B.Guler, A.S.Avestimehr, and P.Mohassel. Coded-privateml: A fast and privacy-preserving framework for distributed machine learning. CoRR , 2019. [36] C. Juvekar, V

References [1] Garapati SS, Hadjiiski L, Cha KH, et al . Urinary bladder cancer staging in CT urography using machine learning. Med Phys. 2017;44(11):5814-5823. [2] Zhu X, Ge Y, Li T, et al . A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys. 2011;38(2):719-726. [3] Carlson JN, Park JM, Park SY, et al . A machine learning approach to the accurate prediction of multi-leaf collimator positional errors. Phys Med Biol. 2016;61(6):2514-2531. [4] Zhu X, Ge Y, Li T, et al . A planning quality evaluation tool for

National Football League games via linear-model methodology. Journal of the American Statistical Association , 516-524. Joseph, A., Fenton, N., & Neil, M. (2006). Predicting football results using Bayesian nets and other machine learning techniques. Knowledge-Based Systems , 544-553. Landers, J., & Duperrouzel, B. (2018). Machine learning approaches to competing in fantasy leagues for the NFL. IEEE Transactions on Games , 159-172. Maher, M. (1982). Modelling association football scores. Statistica Neerlandica , 109-118. McCabe, A., & Trevathan, J. (2008). Artificial

References 1. Topline, F. Core Trends Survey Do You Use the Internet or Email, at Least Occasionally ? – Do You Access the Internet on a Cell Phone, Tablet or Other Mobile Handheld Device, Vol. December 2012 , January 2018, pp. 1-9. 2. Lim, K. H. Risk Factors of Home Injury among Elderly People in Malaysia. – Asian J. Gerontol. Geriatr., Vol. 9 , 2014, No 1, pp. 16-20. 3. Cheng, L., Y. Guan, K. Zhu, Y. Li. Recognition of Human Activities Using Machine Learning Methods with Wearable Sensors. – In: Proc. of IEEE 7th Annu. Comput. Commun. Work. Conf. (CCWC’17