From classical Geodetic Theory to modern Machine Learning: an introduction.
Co-organized by ESSI6/G7
In this short course we first provide a broad overview of geodetic theory, addressing different mathematical problems and well-established solutions adopted in Geodesy. Therefore, we highlight gaps in the current theoretical framework and introduce machine/deep learning paradigms as potential alternative to classical solutions. In this way, we further discuss key relationships between statistical learning and ML/DL methods, in particular focusing on fundamental issues in the adoption of AI techniques as "black box" solutions. Hence, we provide a clear understanding of the major pitfalls, especially concerning the quantification of uncertainty and confidence levels for ML/DL solutions.
Ultimately, we highlight the key role in science of 'explainability' and 'reproducibility', both often overlooked when adopting AI techniques in Geodesy. Target audience is Geodesy and Earth-science practitioners who deploy or evaluate ML in their research works. The format is 60 minutes (e.g. lunch slot) with 30′ for a mini lecture on theoretical fundamentals, 20′ live demo with relevant geodetic examples, and 10′ for Q&A.
Prerequisites: basic linear algebra; no prior ML/DL knowledge is required.
The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.