Seminar on Machine Learning Systems

Overview

The seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for MLs, etc. The focus will be to cover fundamental ideas on ML systems, with an emphasis on software systems and platforms.

The seminar will start on September 29th with an overview of the general topics and the intended format of the seminar. The seminar will consist of student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done alone or in teams (of two people). Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. The talk is split into a 15 minutes presentation of the technical facts (by one person) and 15 minutes critical analysis (by the other person). Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology. Each team is requested to hand in a detailed review form (explained in the first lecture) summarizing the paper, the critical analysis and discussion performed during the presentation slot.

Attendance to the seminar is mandatory to complete the credit requirements. Active participation is also expected, including having read every paper to be presented in advance and contributing to the questions and discussions of each paper during the seminar. 

The seminar will be held in a hybrid format -- all lecturers will be in the classroom (CAB H52) and you are welcome to join in person. In the meantime, we will also stream the lecture via Zoom (link will be provided via email).