Literaturhinweise
Verwendete Literatur im Überblick
- Russell, Stuart J, Peter Norvig, and Ming-Wei Chang. Artificial Intelligence a Modern Approach. Fourth Edition, Global Edition. Harlow, England: Pearson, 2022. Print.
- Cecilia, A. et al. Human-Centered Data Science: An Introduction. MIT Press, 2022.
- Computingeducation. “So Lernen Maschinen!,” November 20, 2019. https://computingeducation.de/proj-ml-uebersicht/.
- Hazzan, Orit, and Koby Mike. “The Data Science Workflow.” In Guide to Teaching Data Science: An Interdisciplinary Approach, edited by Orit Hazzan and Koby Mike, 151–63. Cham: Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-24758-3_10.
- Sammut, Claude, and Geoffrey I. Webb. Encyclopedia of Machine Learning and Data Mining. 2nd ed. Springer Publishing Company, Incorporated, 2017.
- Mahoney, Trisha, Kush R Varshney, and Michael Hind. AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning. O’Reilly Media, Inc., 2020.
- Mehrabi, N., F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan. “A Survey on Bias and Fairness in Machine Learning.” ACM Computing Surveys 54, no. 6 (2021). https://doi.org/10.1145/3457607.
Last modified: Monday, 2 December 2024, 1:21 PM