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Deep Learning for Temporal Information Processing

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Temporal information process covers a broad class of learning problems where knowledge can be acquired from data of a sequential order, e.g. speech modelling, image sequence analysis, robot navigation, financial prediction, and so on. In addition, different learning paradigms, supervised, unsupervised, and reinforcement styles, may be involved in temporal information processing. Recent studies suggested that deep learning has several advantages for temporal data analysis and information extraction regardless of a learning paradigm and can overcome a number of weaknesses of existing temporal information processing methods.

In this project, the following issues will be investigated: 1) Exploration of novel deep architectures for effective encoding temporal information, 2) Model selection issues in the context of the hybrid learning strategy for different learning paradigms, 3) Highly non-linear temporal coherence/factor analysis with different strategies, and 4) Applications in selected real world temporal information processing tasks, e.g., audio stream analysis and financial/stock data mining. 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.

In order to take this project, it is essential to have good background knowledge in mathematics, machine learning as well as good programming skills. If you are interested in this project, please first visit my research student page: for the required materials and information prior to contacting me.


Supervisor's Webpage

How good is research at The University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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