About the Project
The project is going to investigate and develop a generic approach to information component analysis for perceptual data with state-of-the-art machine learning techniques, deep learning. Surrounding the main theme on how to disentangling/extracting information components, main issues to be studied include objective-driven high level abstraction of perceptual data in flexible representation forms, novel building blocks and deep learning models including architectures and learning algorithms to carry out an information component "filter’ and theoretic information aspects in measuring the extracted information components. For demonstration, an information component analysis prototype would be developed for a real application, e.g., speech or facial information component analysis. In general, this project is suitable for one who is interested in fundamental research in machine learning while it is acceptable for one who has a relevant application problem in mind and wishes to tackle their problems with an emerging technology such as deep learning.
It is worth highlighting that the hypotheses set in this project are original and hence this is an extremely challenging project of a great novelty. In order take this project, thus, it is essential to be highly self-motivated and to have excellent background knowledge in mathematics, machine learning, image or speech signal processing and good programming skills. If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.
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