A brisk walk through Deep Learning.
Speaker
Prof. Hujun Yin
Agenda
6:30pm - Webinar begins
8:00pm - Ends
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Meeting ID: 334 104 7804
Passcode: Welcome-23
Synopsis
AI has abruptly landed in the public domain, creating both excitement and fear. Before its seemingly sudden emergence, researchers have long been working on advanced learning methods and effective ways for dealing with large amount of data of increasing complexity, dimensionality and volume.
Whether it is in biology, social sciences, engineering, robotics or computer vision, data is being sampled and cumulated in unprecedented speed and scale. Systematic and automated ways of representing and hence classifying data are becoming a great challenge.
This talk will take you for brisk walk on the AI/ML lane, from its origin to its current boom.
While deep learning has become the mainstream methodology of AI and solutions for many data-driven tasks, esp. vision, with abundant architectures being developed, making sense of deep learning with the manifold concept can help elucidate the underlying relationships that it is uncovering and reveal its possible shortfalls or instabilities. Manifold concept plays an important role in data representations, not only because of the manifold hypothesis, but also its hidden basis for many learning tasks, esp. generative models.
How well a deep network or its feature maps can capture the intrinsic properties of data will determine the capability and performance of the network. Crude, enduring training may not always guarantee a good representation. Instead, organised feature maps can help optimise and explain outcomes of the network. Examples and case studies will be used to illustrate the manifold concept in a wide range of data-driven methods and learning techniques.
About the speaker
Prof. Hujun Yin
Prof. Hujun Yin is a Professor of Artificial Intelligence at the University of Manchester. He has also been the head of Business Engagement in AI and Data for the Faculty of Science and Engineering for four years. His research areas include AI, machine learning, deep learning, signal/image processing, pattern recognition, time series modelling, bio-/neuro-informatics, and interdisciplinary applications. He has supervised over 30 PhD students and published over 200 peer-reviewed articles. Prof. Yin has received over £6 million funding from UK research councils, EPSRC, BBSRC, Innovate UK and industries over 30 projects. Many of his projects involve industries and SMEs in developing cutting edge AI solutions. He has served or has been serving as an Associate Editor for IEEE Transactions on Neural Networks, IEEE Transactions on Cybernetics, IEEE Transactions on Emerging Topics in Computational Intelligence, and the International Journal of Neural Systems. He has also served as the General Chair or Programme Chair for a number of international conferences in AI, machine learning and data analytics. He is the chair of IEEE CIS UK & Ireland Chapter, a senior member of the IEEE, and a Turing Fellow of the Alan Turing Institute.
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