Teaching
Our team contributes to teaching and curriculum development at Lund University within the areas of geospatial AI and data science. We offer guest lectures, workshops, and supervision as part of university education and outreach initiatives, and engage in educational collaboration with partner universities and research networks.
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Machine Learning for Water Engineers
VVR080F, 7.5 credits
The aim of the course is to introduce machine learning algorithms for water applications. The course includes lectures as well as laboratory sessions on the Python programming language for students lacking sufficient programming skills.
View course detailsAim
The aim of the course is to introduce machine learning algorithms for water applications. The course includes lectures as well as laboratory sessions on the Python programming language for students lacking sufficient programming skills. The course is multidisciplinary involving guest lecturers from different departments, covering various applications of machine learning in solving water-related problems e.g., spatial and temporal modeling of water quality and quantity, as well as developing early-warning systems. The course also includes group projects where the students have the opportunity to work on real-world water-related issues and get hands-on experience.
Course Contents
Introduction and foundation of the state-of-the-art machine learning algorithms. Introduction to Python programming language. Computer laboratory sessions to help students get hands-on experience with Python programming language and the application of machine learning algorithms. Guest lectures on the application of machine learning for water-related issues based on the students thesis topics, and available guest lecturers. e.g., spatial and temporal modeling of water quality and quantity, as well as developing early-warning systems. Group projects on real-world water-related problems. Seminar session, and opposition.
Course Literature
Lindholm, A., Wahlström, N., Lindsten, F. & Schön, Thomas B.: Machine Learning, A First Course for Engineers and Scientists. Cambridge University Press, 2022.
The book Machine Learning: A first course for engineers and scientists is suitable for engineering students. The literature also includes three other books on machine learning and Python programming as well as scientific papers:
- Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten and Thomas B. Schön: Machine Learning, A First Course for Engineers and Scientists. Available online at smlbook.org.
- Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H., 2009. The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
- Géron, A., 2022. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc.
- Downey, A., Wentworth, P., Elkner, J. and Meyers, C., 2016. How to think like a computer scientist: learning with python 3. Available online at openbookproject.net.
- Scientific papers will also be used in the course based on the students' disciplines.