These resources are part of the course materials and they can be included in Weekly quiz and Final quiz assessment. 


Articles & Papers  

F. Martínez-Plumed et al., "CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3048-3061, 1 Aug. 2021, doi: 10.1109/TKDE.2019.2962680. 

CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still the de facto standard for developing data mining and knowledge discovery projects. 

Read More https://ieeexplore.ieee.org/abstract/document/8943998?casa_token=9OT474gv8pMAAAAA:sr9T5gLwYVa45qhpB-8Q1x8QuEz-pXTR3rkQHxyqDO1d3G527meuTrAX6TxZCNFJVFsbPiNznBw 

 

Duda, S., Hofmann, P., Urbach, N. et al. The Impact of Resource Allocation on the Machine Learning Lifecycle. Bus Inf Syst Eng 66, 203–219 (2024). https://doi.org/10.1007/s12599-023-00842-7 ;

An organization’s ability to develop Machine Learning (ML) applications depends on its available resource base. Without awareness and understanding of all relevant resources as well as their impact on the ML lifecycle, we risk inefficient allocations as well as missing monopolization tendencies. 

Read More https://link.springer.com/article/10.1007/s12599-023-00842-7 


APA Style: Hoerl, R. W., & Redman, T. C. (2023, December 19). What managers should ask about AI models and data sets. MIT Sloan Management Review. 

Retrieved from https://sloanreview.mit.edu/article/what-managers-should-ask-about-ai-models-and-data-sets/


Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru 

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. 

Read More https://arxiv.org/pdf/1810.03993