Introduction to machine learning in Geosciences
How is Artificial Intelligence (AI) being used in Geosciences today? This block course (under MGEO005 and MGEO006) will answer this question by introducing the capabilities of AI and, more specifically, machine learning techniques for geoscience applications. The fusion of AI not only enhances the efficiency of geoscience workflows but also accelerates processes significantly. In this course, the implementation of both shallow and deep learning approaches is showcased across various (simple) geoscience examples, including image segmentation, porous media property estimation (e.g., permeability), image resolution enhancement, and porous media reconstruction.
Participants in this course not only gain proficiency in programming but also acquire a thorough understanding of the diverse applications of machine learning within this framework. No deep knowledge of programming is necessary because what one needs from the Python language will be taught first. The course also introduces participants to one of the well-established deep-learning libraries, Pytorch. So, it can be a very good start for those one to work later at the forefront of the AI field in their future projects/theses.
The course will be a combination of a presentation and hands-on exercises in the class (like a workshop), allowing students to not just learn but truly immerse themselves in the content. The final grade will be evaluated based on active class participation (like group activities or solving small exercises) and a final term project on a real case study with 4-week due date (written report). All master and PhD geoscience students (geology, geophysics, mineralogy, biogeology, environment and geo-resource management) and other related branches are welcome to this course.
Wichtig/Important:
Die Anmeldefrist für die Prüfung beträgt auch für Blockkurse 10 Wochen nach Vorlesungsbeginn, im SoSe 2024 Dienstag 02.04.2024, 09:00 Uhr - Dienstag, 11.06.2024, 24:00 Uhr.
You have to register for the exam until June 11!
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