Computer Science 692S - S-Systems for Machine Learning

Advances in machine learning (ML) and deep learning are constantly transforming prototypes in research labs to valid solutions to real-world problems. Using ML entails developing end-to-end pipelines to collect data, preprocess it, and run learning and inference algorithms in a scalable manner. This results in computationally intensive workloads and complex software pipelines. Systems for ML help users organize their data and scale these computationally intensive problems to larger and larger datasets. This seminar will review cutting-edge research on these topics. It will focus on reading, presenting, and discussing recent papers in the domain of ML for systems (1 credit). The instructor will offer some 3-credit follow-up independent studies.

Open to graduate Computer Science and Electrical and Computer Engineering students only. SEC 01=3 Cr.; SEC 02=1Cr. No formal prerequisites, but students are expected to have a strong background in graduate-level deep learning and in at least one systems area, such as operating systems, data systems, or systems for deep learning. Students in the 3-credit section are also expected to have strong programming skills and hands-on experience in one system area. Students needing special permission must request overrides via the on-line form: https://www.cics.umass.edu/academics/course-overrides.