Overview
This Extreme learning to handle 'Big Data' PhD study at Cranfield University will address this research challenge. This implies that mathematical modelling of such data is infeasible. The data-driven modelling approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning.
A typical caveat of data-driven modelling using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new process configurations.
Features
- In practice, this might not be possible, that is the sample data could cover only some space, not entire space, of process parameters.
- Integrating prior knowledge into the learning could enable accurate generalization of the data-driven model even when the space of system parameters is only sampled sparsely.
Programme Structure
Curriculum:
- Consequently, it will improve the performance of the learning.
- Integration of the prior knowledge of the system into the learning procedure will be quite challenging since the key enabler of its very powers is the universal approximation capabilities.
- Sampled data are generally noisy, outliers occur, and there always exist a risk of overfitting corrupted data.
- Therefore, the learned function may violate a constraint that is present in the ideal function, from which the training data sampled.
Key information
Duration
- Full-time
- 36 months
Start dates & application deadlines
Language
Delivered
Disciplines
Data Science & Big Data View 27 other PhDs in Data Science & Big Data in United KingdomAcademic requirements
English requirements
Student insurance
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- Additional medical costs (i.e. dental)
- Repatriation, if something happens to you or your family
- Liability
- Home contents and baggage
- Accidents
- Legal aid
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Other requirements
General requirements
- For this research studentship, we are seeking a talented graduate, having (or be expected to obtain) at least an upper second class honours degree (first class honours preferred), MSc or equivalent in Mechanical, Electrical Engineering, Control Engineering, Aerospace or Computer Science.
- Good mathematical background and experience with Matlab/Simulink, C++ and real time implementations and programming would be most desirable.
Tuition Fee
-
International
19675 GBP/yearTuition FeeBased on the tuition of 19675 GBP per year during 36 months. -
National
4712 GBP/yearTuition FeeBased on the tuition of 4712 GBP per year during 36 months.
Living costs for Cranfield
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
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Scholarships Information
Below you will find PhD's scholarship opportunities for Extreme learning to handle 'Big Data'.
Available Scholarships
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