Machine Learning: What? Why? How? When? … and should we care anyway?
A free public talk presented by Prof. Bogdan Matuszewski of the University of Central Lancashire
With continued advances in mathematical modelling, ever increasing computational power and the recent unprecedented proliferation of shared information (e.g. with reported hundreds of hours of video uploaded to the YouTube servers every minute) or creation of large databases most, notably in biomedicine and astronomy, the growth in automated data analysis is unavoidable. Without the deployment of machine learning techniques, handling rapidly growing data volumes may not be possible.
The talk will focus on the recent advances and fundamentals of machine learning with key ideas and terminology explained. The focus will be on a broad overview of main methodologies, including: supervised, semi-supervised, unsupervised, active and reinforced-learning. The essential concepts of: training, testing, evaluation methodologies and metrics will be briefly explained. The talk will include some historical background as well a discussion of the recent state-of-the-art. A representative sample of machine learning techniques will be briefly introduced, including deep learning.
The talk will include a small number of practical implementation examples to succinctly illustrate the key machine learning concepts.
The Rochester Building is listed as number 12 on this map: https://www.ippp.dur.ac.uk/sites/default/files/pictures/map.jpg. Tea, coffee and biscuits will be available from the Bransden Room from 18:30, the talk will commence at 19:00. All rooms will be signposted.
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