Neurobiology: Wellspring of Machine Learning & A Reflection on the Impact of Machine Learning on Computer Vision
Day 4 – Foundations of Data Science I (AM)
10.00- 11.45 Neurobiology: Wellspring of Machine LearningWe will explore the brain as an information-processing device, and hows its apparent operating principles have influenced Machine Learning.
Speaker biography: Prof Barak Pearlmutter received a BS in Mathematics from CWRU, a PhD in Computer Science from Carnegie Mellon University (where he worked on neural networks the second time they were cool), postdoctoral training in Neuroscience at Yale University, and spent several years in Industry at Siemens Corporate Research and on the faculty at the University of New Mexico. He is currently in the Department of Computer Science at Maynooth University, in Ireland. His main current research interests are two-fold: understanding information processing in the brain, and figuring out how to build artificial systems that exhibit brain-like performance. The focus of the former is currently on exploring criticality in the brain, while the latter is upon building mathematical formalizations and programming languages that support the construction of complex adaptive systems.
11.45-13.00 I can see clearly now – A reflection on the impact of Machine Learning on Computer Vision
The lecture will introduce people new to machine vision to the typical workflows used for recognition. The presentation will use a practical approach and will include several examples. Cloud and edge-based solutions will be demonstrated. The impact of modern approaches based on Machine Learning will be discussed.
Speaker biography: Charles Markham is a graduate of Applied Physics at DCU. His PhD was in the area of element specific imaging in computerised tomography. He has maintained an interest in novel imaging systems, instrumentation, sensing and machine vision technologies. He has collaborated with the Engineering Department and Hamilton Institute at MU to develop a brain computer interface based on optical tomography. Working in collaboration with TU Dublin he developed novel methods of measuring and locating retro-reflective objects in sequences recorded by a custom mobile vision system. He has developed techniques for imaging using coded apertures and developed wide-baseline stereo imaging methods to achieve a visual radar system. Working with the physiotherapy department at UCD, he developed practical sensors for integration into wearable biofeedback systems and has maintained an interest in motion capture (MoCap) . He has also collaborated on multidisciplinary research in measuring driver behaviour and has integrated eye-tracking and EEG sensors into novel driving simulators. Currently he is developing a research interest around modelling invasive species and the spread of wildfires. He is an active member of the MU Mathematics and Statistics Ecology group. He teaches Robotics, Computer Graphics and Advanced Computer Architecture.
Participants are welcome to join in person (S206 Schumann Building, University of Limerick) or remotely. A Zoom link will circulate to remote participants on Wednesday 24th of August.
There is a 0.5 deduction from the training unit allocation per Enterprise Alliance member joining this session.
The SFI Centre for Research Training in Foundations of Data Science will train a cohort of PhD students with world-class foundational understanding in the horizontal themes of Applied Mathematics, Statistics, and Machine Learning.
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