Description
Despite their high popularity in both academic and industrial applications, machine learning (ML) and AI usually give the impression of blackbox models, existence of artifacts, counterintuitive results, diversity of methods, complicated training, etc. In this talk, I will share my research and teaching experiences in ML/AI in order to promote the importance of building up intuitive and comprehensive understanding of ML/AI methods for students and researchers who are interested in applying ML/AI to their studies.
