The Machine Learning Group is a cross-faculty network of Imperial College’s Department of Computing. We embrace research at the interface of machine learning, artificial intelligence and its Big Data applications.
With an ever-increasing use of Internet, digital devices and science, tremendous amount of data encapsulating valuable knowledge have become available. We reflect this impact in the many vibrant facets of this field from automated reasoning to probabilistic inference, from creative and affective computing to human-computer interaction, from machine vision to neurotechnology, from bioinformatics to medical & economic applications. Broadly members of the group belong to at least one of the two pillars of Machine Learning:
- Data-level machine learning to support feature extraction from data (“Big Data”)
- Knowledge-level machine learning and knowledge representation to extract readable and insightful relational knowledge which supports human-understandable machine inference
At the data-level, ongoing research focuses on applying a wide variety of feature-based machine learning techniques in key application areas. Notable recent successes in these areas include the application of machine learning to medical imaging of the brain and heart (Rueckert), human emotions and social signals (Pantic, Zafeiriou), robotic vision (Davison), autonomous systems (Deisenroth), medical applications (Gillies), computational neuroscience and Brain-Machine-Interfaces (Faisal).
At the knowledge-level, our key expertise lies in Relational and First-Order Logic Learning. Past research had major impact in scientific discovery in biological prediction tasks (Muggleton), security and semi-automated software engineering (Russo). Moreover, the closely related areas of smart analysis of biological or economic network topologies (Przulj) and robust systems optimisation (Parpas) and scalable data analytics (Pietzuch).
We are training leaders in the highly sought after domains of data scientists. Our MSc program (Specialisation in Machine Learning) and PhD training will enable students to unlock this knowledge by means of sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures as to advance the progress in various application domains, including neurotechnology, bioinformatics, security and human-centered computing.
Please contact the group leader, Aldo Faisal, for further details on the Machine Learning Group and how to join.