CSE 291D / 234: Data Systems for Machine Learning (Online-Only Edition)!!! This website is archived. Please see the website of the latest edition of this course among the links listed here. !!! Lectures: TueThu 1:00-2:20pm PT @ Zoom only (link posted on Piazza page) Instructor: Arun Kumar
Teaching Assistant: Htut Khine Win
Piazza: CSE 291D/234 (Requires access code posted on Canvas) Announcements
Course Goals and ContentThis is a research-based course on data systems for machine learning (ML), at the intersection of the fields of ML/AI, data management, and systems. Such systems power modern data science applications on large and complex datasets, including enterprise analytics, recommendation systems, and social media analytics. Students will learn about the landscape and evolution of such systems and the latest research. This is a lecture-driven course with quizzes, exams, and paper reviewing components for evaluation. It is primarily tailored for MS students, PhD students, and advanced undergraduates interested in the state of the art of systems for scalable data science and ML engineering. This course will cover key systems topics spanning the whole lifecycle of ML-based data analytics, including data sourcing and preparation for ML, programming models and systems for scalable ML model building, and systems for faster ML deployment. Emerging topics such as governance, explanation, and ethics of ML systems will likely be covered too. A major component of this course is reviewing cutting edge research papers from recent top conferences on these topics. See the course schedule page for the entire list of topics, as well as the paper reading list. Course Format and Online-only Modality Instructions
Prerequisites
Suggested Textbooks
Exam Dates
Grading
CutoffsSince this is the very first non-project and online-only edition of this course, the grading scheme is a hybrid of absolute and relative grading to mitigate the "cold start" issue. The absolute cutoffs are based on your absolute total score. The relative bins are based on your position in the total score distribution of the class. The better grade among the two (absolute-based and relative-based) will be your final grade.
Non-Letter Grade Options: You have the option of taking this course for a non-letter grade. As per the CSE department's guidelines, the policy for P in a P/F option is a pass-equivalent letter grade, i.e., D or better; the policy for S in an S/U option is a letter grade of B- or better. Classroom Rules
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