Scalable Time-series Classification

Abstract: Time-series classification is a pillar problem for the machine learning community, particularly considering the wide range of applicable domains. In this talk, the focus is on prediction models that are scalable both in terms of the training efforts, but also with regards to the inference time and memory footprint. Concretely, time-series classification through models that are based on discriminative patterns will be presented. Finally, the talk will end with a recent application on biometric verification.

Bio: Dr. Josif Grabocka is a Postdoc at the University of Hildesheim, Information Systems and Machine Learning Lab, working in the research team of Prof. Dr. Dr. Lars Schmidt-Thieme. He graduated his PhD in Machine Learning from the University of Hildesheim in 2016. Dr. Grabocka's primary research interests lie on mining time-series data and more recently on Deep Learning techniques for sequential data.