Found 9 relevant results in 2.30s where lecturer="Maarten Jan Van Strien"

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103-0377-10L 2020W , 2021W , 2022W , 2023W , 2024W , 2025W 3 Credits MSC D-BAUG

The course Basics of RE&IS equips Master’s students with core skills in scientific writing. Central is the full writing cycle, supported by peer- and self-review. Students learn to critically evaluate literature, argue pros and cons, create simple visualizations, and present their work. Generative AI is examined for its potential and risks at each step.

2020W
2021W
2022W
2023W
2024W
101-0522-10L 2020W , 2021S , 2021W , 2022S , 2022W , 2023W , 2024W , 2025S , 2026S 1 Credits DR D-BAUG

Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations.

2020W
2021S
2021W
2022S
2022W
2023W
2024W
2025S
101-0523-15L 2024W , 2025S , 2026S 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

2024W
2025S
101-0523-14L 2023W 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-11L 2021S 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-12L 2021W 1 Credits DR , MSC D-ARCH , D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

101-0523-13L 2022W 1 Credits DR D-BAUG

This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms).

103-0378-00L 2021W , 2022W , 2023W , 2024W , 2025W 3 Credits MSC D-BAUG

R is one of the most popular programming language in science and practice for data analysis, modelling and visualisation. In this course, you will learn the basics of R and some common applications of R, such as making plots, regression analysis and working with spatial data. The weekly computer labs start with a short lecture followed by exercises that have to be handed in to pass the course.

2021W
2022W
2023W
2024W
103-0307-00L 2020W 3 Credits MSC D-GESS , D-BAUG

Planners need to make decisions about the best possible mix of land uses. With increasing availability of spatial databases and the analytical capabilities of GIS, more effective decision support systems can be developed. The goal of the course is to provide the basics of spatial analysis and to teach the integration of spatial data into multicriteria decision-making systems.