Machine learning is the study of computational processes that find patterns and structure in data. Our group is interested in a broad range of theoretical aspects of machine learning as well as applications. Much of the current excitement around machine learning is due to its impact in a broad range of applications. The applications considered in our research include astronomy, systems biology, neuroscience, natural language processing, robotics, and computer vision. Faculty Members Research interests Amos Storkey Continuous time systems, deep learning, stochastic differential equations Iain Murray Bayesian statistics, approximate inference, Markov chain Monte Carlo, scientific data analysis Siddharth Narayanaswamy Explainable and Interpretable ML, Bayesian program learning, Vision and Language, Probabilistic programming, Neuro-symbolic systems, Approximate inference, Human-machine interaction. Nigel Goddard Probabilistic modeling of energy-related systems Chris Bishop Graphical models, variational methods, pattern recognition Michael Gutmann Efficient statistical learning, inference for complex models, unsupervised deep learning, natural image statistics, computational biology Arno Onken Probabilistic models, in particular copula-based models; Dimensionality reduction techniques; Information theory; Applications to biological systems Ava Khamseh Semi-parametric probabilistic modelling, targeted learning, causal inference and its applications to population biomedicine and cancer research Oisin Mac Aodha Human-in-the-loop machine learning, machine teaching, deep learning, and computer vision Nikolay Malkin Bayesian machine learning and generative modelling, amortised inference for (neuro-)symbolic models, probabilistic reasoning/planning in language and formal systems, AI for science and mathematics. Antonio Vergari Efficient and reliable machine learning in the wild, tractable probabilistic modeling, combining learning and reasoning Chris Williams Gaussian processes, image interpretation, unsupervised learning, deep learning, time series models Events We have two journal reading groups: PIGS PIGlets We also have the following weekly: Brainstorm Coffee Machine Learning Lunch Joining the group If you would like to join the machine learning group as a PhD student, please see this information: Prospective Postgraduates Occasionally we have openings for postdoctoral researchers. Please contact the individual lecturers directly about this. Classes As part of our MSc programme, we teach a large number of classes in machine learning, namely: Applied Machine Learning Probabilistic Modelling and Reasoning Machine Learning and Pattern Recognition Data Mining and Exploration Neural Information Processing Machine Learning Practical Related Research @ Edinburgh Many other research groups at Edinburgh work actively in related areas, including: ILCC (statistical natural language processing) IPAB (vision and robotics) ICSA (self-managing compilers and computer systems) BioSS (bioinformatics, statistics) School of Mathematics (statistics) Some of these links are represented by: Informatics Research Programme on Machine Learning Funding We receive funding for our research from many sources, including: Engineering and Physical Sciences Research Council Microsoft Research PASCAL Biotechnology and Biological Sciences Research Council This article was published on 2024-11-22