Machine Learning Lab Launch
The vision of the Machine Learning Lab is to develop autonomous decision-making systems, which close the perception-action-learning loop while learning from small amounts of data.
Therefore, the Machine Learning Lab will promote and lead scientific advances in data-efficient machine learning, i.e., the ability to learn in complex domains without requiring large quantities of data. Research areas that fall into this category include probabilistic modelling, incorporation of domain or structural prior knowledge, transfer learning, semi-supervised learning, active learning, Bayesian optimization and reinforcement learning.
Agenda – to follow
Data Science Seed Funding call in Probabilistic Modelling
To celebrate the launch of the DSI Machine Learning Lab, the Data Science Institute is pleased to announce its first Data Science Seed Funding call for pilot projects in the area of probabilistic modelling.
Background
On 1 March 2018, the Data Science Institute will launch the Machine Learning Lab, headed by Dr Marc Deisenroth. The Lab’s aim will be to bring together academics from across all the faculties at Imperial College who have an interest in Machine Learning and to catalyse new research and funding applications in this field. To support the new Lab, the first call for the Data Science Seed Fund will be focused on probabilistic modelling.
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Funding opportunity |
Data Science Institute Seed Fund – Probabilistic Modelling |
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Key dates |
13th April, 12 Noon GMT |
Deadline for submission of outlines to a.ashley-smith@imperial.ac.uk |
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Funding available |
Up to £20k per project. Project durations between 1-5 months |
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Contact |
Administrative contact: Alice Ashley-Smith (a.ashley-smith@imperial.ac.uk) Academic contact: Marc Deisenroth (m.deisenroth@imperial.ac.uk) |
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Additional information |
http://www.imperial.ac.uk/data-science/research/multidisciplinary-labs/machine-learning-lab/ |
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Scope
The Data Science Institute Seed Fund has been set up to support pilot projects in data science. The aim of the fund is to enable DSI Fellows to obtain preliminary results and data which can then be used in applications for further research funding from charities, RCUK or government bodies.
Probabilistic modelling is an important part of data-efficient machine learning and statistical inference when data is scarce. Accounting for uncertainty in modelling, prediction of long-term consequences and decision making is critical in these situations. Application areas can include healthcare, robotics, optimization, finance and sentiment analysis.
Application Process
Applications should have a Principal Applicant based at Imperial and should contain:
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· A completed Data Science Research Institute Seed Fund Application Form which includes:
– A written case for support
– A justification for resources
– Relevant experience of applicant and co-applicants
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· A one page financial summary: applications should provide information on the requested costs using an InfoEd statement approved by the Department. As InfoEds will not be submitted and will remain as draft, an email confirming departmental approval should also be included in the application, however, proposals do not need to be reviewed by Research Services prior to submission
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· CV of the Principal Investigator and Co-Investigators.
Call guidance and documentation can also be found on the Machine Learning Lab webpage.
If you are interested in joining the DSI as an Academic Fellow, more information can be found here.
Please disseminate this information throughout your networks as necessary