Computer Science 692S - S-Systems for Machine Learning

Over the last few years, a wave of excitement about machine learning (ML) and deep learning has proliferated from academia to industry, transforming prototypes in research labs to valid solutions to real-world problems. Using ML entails developing end-to-end pipelines to collect data, clean it, and run learning and inference algorithms in a scalable manner. This results in computationally intense workloads and complex software pipelines. Systems for ML help users organize their data and scale these computationally intense problems to larger and larger datasets. At the same time, ML is having an increasing impact on systems design. Fine-tuned analytical heuristics and cost models are being replaced by learned models, following trends observed in other fields. This seminar will review cutting-edge research on these topics and allow students to work on a hands-on project. It will focus on reading, presenting, and discussing recent papers in the domains of ML for systems and systems for ML (1 credit) and a final project focusing on a specific ML system topic (3 credits).

Open to Graduate Computer Science students only. SECT 01=3 CR; SECT 02=1 CR. SEATS HELD FOR INCOMING GRAD STUDENT REGISTRATION. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA THE ON-LINE FORM: https://www.cics.umass.edu/overrides.