Topics in Machine Learning Systems

The course will cover recent papers from the literature in the emerging area of ML systems. With abundance of data and the emergence of
data science, machine learning is widely applicable in a variety of usage scenrios with high performance, accuracy and cost being key
design goals. The latter not only has implications for algorithms but also platforms from software to hardware to enable collective
optimization of the design metrics. The topic is inherently multidisciplinary and will cover papers from a variety of conferences in computer
science subfields; e.g., ICML, NIPS, ICLR, KDD, VLDB, SIGMOD, SOSP, OSDI, SysML, ASPLOS and ISCA).

 

Students will understand the state-of-the-art in the emerging area of ML Systems. Specifically the cover core technologies in production ML
systems including: (1) languages and paradigms for specification of large-scaling machine learning applications, (2) the convergence of
analytics from relational databases to unstructured data, (3) resource management in large-scaling ML systems, (4) network stacks for ML
systems, and (5) emerging ML systems accelerator architecture.

In this course students learn to read, understand, critique and present research papers.