Data Science and Artificial Intelligence for Engineers

Module Introduction

Data Science (DS) and Artificial intelligence (AI) are poised to revolutionize all aspects of Civil Engineering, Environmental Engineering and Applied Earth Sciences. Our interdisciplinary module in Data Science and Artificial Intelligence for Engineers (DSAIE) will offer 2nd year Masters students the opportunity to learn these powerful tools and apply them from the very beginning.

We will focus primarily on Probabilistic Machine Learning and Deep Learning methods, effective data-driven approaches to deal with the uncertainty and the complexity characterizing the challenges of the interconnected natural, living and built environments. We will start by revisiting what we did in the MUDE, and we will end by exploring the latest trends in Generative AI and Large Language Models.

During this module, the students will broaden their engineering understanding through collaboration with peers from diverse backgrounds, following instruction from experts across various programs, and applying what they learn on real-world case studies. The course will also provide plenty of opportunities to further develop their Python coding skills through hands-on exercises and workshops.

By the end of the module, the students will have acquired a practical and holistic understanding of DS/AI and its applications in their fields of choice, which will be extremely valuable for the student future careers.

Module Units

The proposed cross-over module is divided into three units, of similar size:

Unit 1. Probabilistic Machine Learning [LOS 1, 3, 4]

Unit 2. Deep Learning [LOS 1, 3, 4]

Unit 3. Project [LOS 2, 3, 4, 5, 6]

Formative assessment (feedback)

Unit 1 and 2: Computer-based quizzes with multiple answers for each topic, including coding exercises. These will be made available every week. A mock-up theory exam will be made available in Week 6.
Unit 3: Formative feedback provided regularly by project supervisors; peer-review via Buddy Check. Mid-term presentation in Week 5.

Summative assessments:

There will be two summative assessments:

• An ANS written exam for the theoretical part

• A group presentation with interview for project evaluation.

Students understanding of theoretical content/coding skills will also be assessed as part of the project learning line, specifically their ability to apply the theory, implement their solution and reflect on the meaning and limitations of produced results. This justifies the equal value of the two summative assessments (i.e., 50% each).

The project deliverables include a shared final presentation, a shared code repository, and an individual reflection on the contribution (max 150 words). The grade will mainly reflect the originality and impact of the final solution, the quality of presentation, and the quality of code repository/documentation. A detailed grading rubric is made available at the beginning of the module.

All subgrades must be >5.8 to pass the module.

For more information on grading, see article 14 in the Rules and Guidelines (RGBE):
https://www.tudelft.nl/en/student/ceg-student-portal/education/education-information/educational-rules-and-regulations