Digitalisation, Data Science and Machine Learning in Water Engineering (250MAG001) – Course 2025/26 PDF
Syllabus
Learning Objectives
Specialization subject where knowledge in specific skills is intensified. Knowledge at a specialization level that should allow the development and application of advanced techniques and methodologies. Master's level specialization contents related to research or innovation in the field of engineering. This subject aims to provide an insight into the possibilities offered by machine learning techniques, digitalization and artificial intelligence in disciplines related to water engineering.
Total hours of student work
| Hours | Percentage | |||
|---|---|---|---|---|
| Supervised Learning | Large group | 45h | 100.00 % | |
| Self Study | 80h | |||
Teaching Methodology
The course consists of 4 hours per week of classroom activity. Two hours are devoted to theoretical lectures, in which the teacher presents the basic concepts and topics of the subject, shows examples and solves exercises. Two hours are devoted to problem-solving using specific software with increased interaction with students. Practical exercises are conducted to consolidate the general and specific learning objectives.
Grading Rules
The evaluation calendar and grading rules will be approved before the start of the course.
Practical exercises proposed and solved in class (30% of the grade) Final exam to evaluate the knowledge obtained (70%)
Office Hours
To be agreed with the student. You must make an appointment in advance.
Bibliography
Basic
- Bishop, Christopher M. Pattern recognition and machine learning. New York: Springer, cop. 2006. ISBN 0387310738.
- Brunton, Steven L; Kutz, Jose Nathan. Data-driven science and engineering : machine learning, dynamical systems, and control. 2nd ed. Cambridge: Cambridge University Press, 2022. ISBN 9781009098489.