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Field
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Structures CSE 031: Computer Organization and Assembly Language CSE 100: Algorithm Design and Analysis CSE 107: Introduction to Digital Image Processing CSE 108: Full Stack Web Development CSE 111: Database
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: Algorithm Design and Analysis CSE 107: Introduction to Digital Image Processing CSE 108: Full Stack Web Development CSE 111: Database Systems CSE 120: Software Engineering CSE 160: Computer Networks CSE 168
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that include symbolic AI and formal methods (logic, theorem provers, type systems, categories, etc.), concurrent programming languages (choreographic programming, session types, etc.), distributed computing
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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employers. About the Global Campuses The university campuses operate as a distributed global network. For example, new programs may be developed and established at one of the network campuses, enhanced
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the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models. However due to the complexity
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65