ML-based Systems
Description: This lecture presents the Machine Learning context, its current narrative, and the motivation and definition of the ML-based Systems concept. We start this short course by developing a couple of examples to motivate a systems perspective when designing, developing, and deploying systems based on ML.Department: 34th International Symposium on Statistics
Institution: Universidad de Nariño
Date: July 30, 2025
Hours: 1.5
From: 8:00 am
To: 09:30 am
Resources
Books
- Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach 3rd ed. Prentice Hall
- Deisenroth M. P. et al. (2020). Mathematics for Machine Learning
Papers and Reports
- Turing A. M. (1950). Computing Machinery and Intelligence
- Paleyes, A., et al. (2020). Challenges in deploying machine learning: a survey of case studies
- Kabi J., Maina C. (2021). Leveraging IoT and Machine Learning for Improved Monitoring of Water Resources - A Case Study of the Upper Ewaso Nyiro River
- Cabrera, C., et al. (2022). MAACO: A Dynamic Service Placement Model for Smart Cities
- Shumailov I. et al. (2024). AI Models Collapse when Trained on Recursively Generated Data
Web
- Geoffrey Hinton Lecture at Cambridge (2023)
- Demis Hassabis Lecture at Cambridge (2023)
- The AIAAIC Repository
- Data Science Africa (DSA)