The fourth industry revolution implies the digitalization of many aspects of life, making available huge data sets that are too large and too complex to be dealt by human intelligence. Machine learning, a subset of artificial intelligence, is the science of getting computers to learn and act like humans do by feeding them with big data. The machines learn in an autonomous way and improve themselves over time. Machine learning is successfully applied in everyday life, from Amazon to Netflix passing by Spotify, Google and many more. In this event, real-life cases will be used to introduce you to Machine Learning.
This event is open to everyone. No prior knowledge of statistics, computer science or Machine Learning is necessary.
A networking aperitif will follow this event.
Prof. Dr. Martin Huber
Martin is Professor at the University of Fribourg and holder of the Chair of Applied Econometrics. He will briefly introduce Machine Learning in a non-technical way, discuss perils and promises of Machine Learning and demonstrate an application in which he used Machine Learning to solve a relevant real-world problem, namely the detection of cartels in Switzerland.
Michele Alberti, Ph.D. Candidate
Michele is a researcher at the University of Fribourg and member of the Document Image Voice Analysis Group. He will briefly introduce Deep Learning, a subset of Machine Learning. Primarily, he will present how Deep Learning is able to solve practical real-world issues, such as the analysis of voice and images.