Machine learning uses efficient algorithms to uncover patterns in large data sets and it fundamentally changes our perspective on data processing. What makes a learning algorithm efficient? The goal is to find a balance between sample, model, and computational complexities. Moving algorithms to the quantum domain promises improvements in all three aspects of this balance. This talk gives an introduction to the core concepts in learning theory, and then looks at the major directions in quantum machine learning to analyse what we can gain.

Before the event (from 10:30) we offer a cup of tea and some cookies.