Systems for Machine Learning

COMPSCI 692S (seminar)

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 may offer some 3-credit follow-up independent studies based on well-defined project proposals.

Prerequisites: None. Background on ML and deep neural networks (COMPSCI 589, 689, 682, or similar) is strongly recommended.

Class meetings: Tuesday at 4 PM, LGRC Makerspace. Attendance is mandatory.

Credits: 1 credit. The instructor may offer 3-credit follow-up independent studies based on well-defined project proposals.

Syllabus: The syllabus is available here.

Seminar structure

The course will consist of meetings with presentations. Students will be expected to participate in the following activities.

  • Presentations. Each student will have to present at one of the meetings. The presentation will cover one paper taken from a reading list. Each presentation will have 3 parts:
    1. Background and motivation. Present the general topic addressed in this paper, the prior related work, and the gaps that this paper addresses. Presenters are encouraged to read the most relevant work in the area to prepare this part of the presentation. (~15 minutes)
    2. Paper content. Presentation of the technical content of the paper. (~15 minutes)
    3. Q&A. Propose potential research directions extending the work. Optionally, this section can be presented as a specific project proposal with expected goals and intermediate milestones. (~10 minutes)

  • Paper reviews. Two days prior to each presentation, each student will have to enter a list of questions for the presenter on a Google form. Each student will ask a question during the Q&A session.

  • Independent study proposals. Students who have already presented a paper will have a chance to present an independent study proposal. Proposals are optional. Proposal presentations will follow the same format as paper proposals: 15 minutes for background and motivation for the project, 15 minutes to describe the work that will be done during the project, and a Q&A session.
    • The instructor may offer some 3-credit independent studies based on well-defined project proposals.