Artificial intelligence and machine learning
This broad research area covers interdisciplinary collaboration on topics such as the cognitive sciences, philosophy of mind, and biologically inspired AI.
Artificial intelligence is the study and design of a system that perceives its environment and takes actions that maximize its chances of success.
The subfield of artificial intelligence, Machine learning focuses on algorithms that learn. Our research asks how we can build algorithms that automatically improve with experience, taking into account the fundamental laws that define this process.
Machine learning is an established research discipline. However, recent advances have increased the impact on many areas of society, science, medicine, and everyday life.
Our research covers a wide range of topics, from general AI such as adaptive problem solving, heuristic search, or multi-agent systems, to diverse machine learning areas, such as natural language processing and data streams. We also have interests in geospatial data mining, Bayesian and reinforcement learning, ensembles, recommender systems, matrix factorization, equation discovery, fairness in machine learning, multi-label classification, adversarial learning, and privacy.
This research is applicable across several interdisciplinary areas. In particular, to the fields of bioinformatics, health informatics, computational social science, computational sustainability and cheminformatics.
Find out more about our research and events on the Machine Learning Group website.
Associate Professor Ian Watson