Overview, Syllabus, Structure

This is a graduate-level reading seminar on machine learning and computer systems. In this course we will explore the state of the art in how machine learning is being used in systems, why, and where there are opportunities for further advancement. The objectives of this course are:

The course is structured around lectures by the instructor (Aditya Akella), guest lectures, and paper readings/presentations by the students with open discussion. Students will form a project group (two or three students) and conduct a research project on applying machine learning to systems.


  • Why use machine learning in systems
  • Types of systems problems to which learning applied, examples include:
    • Learned data structures
    • Leanred algorithms
    • Learning system configuration
    • Learning system controllers
    • Simulations and trace generation
    • Dealing with messy and unpredictable environments
  • Open issues, including:
    • Suitable system support to enable low-overhead ML use
    • Lack of compositionality in, and guarantees for, learned algorithms
    • Integrating code and models

    Course organization

    Paper reading response

    The reading list is here. You are required to post reading a response to Ed discussion by 6PM the day before the class. The response include a short summary of each paper, and your opinion of the paper.

    In-class paper discussion

    You will lead the paper discussion in classes. Presentation slides are optional. Your goal is to keep the discussion moving along by providing necessary context and background of the paper.

    Research project

    The course project is an open-ended research project, done in groups of two or three. A list of project ideas will be posted in Canvas. You are required to submit a proposal and a final report. There will be a in-class final presentation.


  • Paper reading response (20%)
  • In-class paper discussion leadership (20%)
  • Research project with a final presentation and report (50%)
  • In-class participation (10%)

    Course policies

    Academic integrity

    All material you submit in this course (reading responses, project reports, and presentation materials) must be your own. If you use someone else’s material, you must cite them properly and make it very clear which parts are your own work. If you are ever in doubt about whether something you intend to submit violates this policy, please contact me before doing so.

    Excused absences and late submissions

    If for any reason you need to miss class or the response deadline, please contact me as soon as possible and at least one week in advance (unless it is an emergency). We will find a way to make sure that your class participation and reading response grade won't be affected.

    Services for students with disabilities

    The university is committed to creating an accessible and inclusive learning environment consistent with university policy and federal and state law. Please let me know if you experience any barriers to learning so I can work with you to ensure you have equal opportunity to participate fully in this course. If you are a student with a disability, or think you may have a disability, and need accommodations please contact Disability and Access (D&A). Please refer to D&A’s website for contact and more information: http://diversity.utexas.edu/disability/. If you are already registered with D&A , please deliver your Accommodation Letter to me as early as possible in the semester so we can discuss your approved accommodations and needs in this course.

    Sharing of course materials is prohibited

    No materials used in this class that are produced by the instructor or by students may be shared online or with anyone outside of the class without explicit, my written permission. Unauthorized sharing of materials may facilitate cheating. The University is aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in initiation of the student conduct process and include charge(s) for academic misconduct, potentially resulting in sanctions, including a grade impact.