CISC 719 Contemp Comput Syst Modeling

Real-world problems entail a hierarchy of systems that interact in complex ways. This causes such complex problems not to lend themselves to easy solutions with computational methods like classical parametric machine learning. The complexity arises from three main causes: high-dimensionality, unknown function properties, and computationally expensive analysis and simulation. These challenges with the presence high volume/velocity streaming data severely aggravate the difficulty and become the bottleneck for any computational solution. This course helps the student to explore some advanced modeling and optimization methods that can help solve such problems. Deep Learning (DL) allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. DL has the ability to discover convoluted structure in large data sets by using say the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. A special emphasis will be put on how to build applications using this approach that have the potential to perform and scale well on a variety of different previously studied parallel computing systems. Extensive use of parallel programming models like CUDA, C, Python, OpenMP and may be Fortran will be to conduct weekly projects.