The aim of the Machine Learning Seminar series is to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.
The seminar is aiming to bring together young and more experienced researchers from various disciplines and to exchange ideas on Machine Learning techniques. The seminar is run under the auspices of the DTU DRIVEN PRIDE project that is funded by the FNR and coordinated by Andreas Zilian, as well as the widening participation DRIVEN project funded by H2020 and led by Stéphane Bordas. It also welcomes talks by researchers from a wider collaborative network, including, but not restricted to, early stage researchers in RAINBOW ITN as well as current and incoming individual Marie Skłodowska Curie fellows.
The usual format is the following: a short presentation (15-20min) followed by a longer discussion (30-40min). The usual time is Wednesdays, 10:00 a.m. (CET).
Please visit the ML seminar website for the schedule of upcoming contributions and a record of past seminars.
If you are interested to join, please contact Jakub Lengiewicz.