Lab 1 is due Sep 2.
This PhD-level seminar explores the design, implementation, and evaluation of systems for machine learning. Topics include hardware and software integration, modern profiling techniques, scalable architectures, and optimization methods. Students will examine current research and technical approaches to construct, assess, and refine systems that support complex machine learning models.
Through a combination of theoretical discussions, lab assignments, and collaborative projects, participants will engage with academic literature and industry practices to address practical engineering challenges. Assessments will include research presentations, hands-on programming assignments, and team projects geared toward developing measurable, robust solutions in the field of machine learning systems.
Professor: Seo Jin Park
Office hours: via appt.
Questions: piazza (To ensure response, do not send emails to instructors directly.)
Time: Friday 1:00 - 4:20pm
Location: KDC 236
Course presentation (30%), programming lab assignments (20%), Final Project (35%), in-class participation (15%)
There are no textbooks for this class.