ML Adoption
Description: This lecture will start looking into the adoption process of ML technologies. The goal is to design ML-based systems that align with our socio-technical systems and their stakeholders, while ensuring its careful development and safe deployment. To this end, we introduce simple but powerful methodologies for designing and developing ML-based systems.Department: 34th International Symposium on Statistics
Institution: Universidad de Nariño
Date: July 31, 2025
Hours: 1.5
From: 8:00 am
To: 09:30 am
Resources
Papers and Reports
- Cabrera C. et al. (2025). The Systems Engineering Approach in Times of Large Language Models
- Bastidas V., Schooling J. (2025). Socio-Technical AI Design For Public Value
- Lavin A. et al. (2022). Technology Readiness Levels for Machine Learning Systems
- Hasterok C., Stompe J. (2022). PAISE® – Process Model for AI Systems Engineering
- Hershey P. (2021). System of Systems Engineering Approach for Complex Deterministic and Nondeterministic Systems (ACDANS)
- Lawrence N. D. (2017) Data Readiness Levels
- Taherdoost H. (2021) Data Collection Methods and Tools for Research; A Step-by-Step Guide to Choose Data Collection Technique for Academic and Business Research Projects
- Boda Bodas and Road Traffic Injuries in Uganda: An Overview of Traffic Safety Trends from 2009 to 2017
Web
- The Data Science Landscape - Advanced Data Science Lecture at Cambridge
- Meet the Data Quality Dimensions
- Advanced Data Science - Visualisation I
- Advanced Data Science - Visualisation II
- OpenML Datasets
- Tensorflow Datasets
- Iris Dataset
- UK Price Paid Dataset
- Open Postcode Geo Dataset
- Open Street Maps API
- Kaggle Datasets
- Registry of Research Data Repositories