Topics in Machine Learning Systems


Lecturer(s) :

Falsafi Babak
Jaggi Martin
Kermarrec Anne-Marie




Every year


Next time : Fall 2020


This course will cover the latest technologies, platforms and research contributions in the area of machine learning systems. The students will read, review and present papers from recent venues across the systems for ua ML spectrum.


The course will cover recent papers from the literature in the emerging area of ML systems. With the emergence of massive data and data science, machine learning is widely applicable in a variety of usage scenarios 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 course will cover core technologies in production ML systems including: (1) languages and paradigms for specification of large-scaling machine learning applications, (2) the convergence of anlaytics from relational databbases to unstructured data, (3) resource management in large-scaling ML systems, (4) network stacks for ML systems, and (5) emerging ML systems accelerator architecture.


ML Systems

Learning Prerequisites

Required courses

Graduate level course in architecture, databases, systems.

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German