Machine Learning

We will address topics both in the development of core ma chine learning (ML) techniques, as well as investigate their use in novel application areas, addressing any challenges arising from the particular characteristics of the application domain.

In recent years we have seen significant advances in ML and neural networks, reinforcement learning, and deep learning in particular, numerous significant challenges remain to be addressed in their applications to a wider range of real-world applications, we will also consider the broader issues raised by the use of ML in practice. We will consider the challenge of transparency and explanation in the context of “black box” learning systems such as deep neural networks. We will consider how in developing real-world systems that address real-world challenges, how other AI techniques need to integrate and interoperate with ML-based methods.