Systems for Machine Learning

Catalog description

The course covers advanced topics in machine learning systems with an emphasis on the interplay between models and system-level support. This course surveys recent advances in efficient machine learning computing techniques including model compression, pruning, quantization, neural architecture search, knowledge distillation, distributed training, and parallelism. Discussion-oriented classes focus on in-depth analysis of readings. Final project and paper required. Appropriate for graduate and advanced undergraduate students. After completing this course, students should be able to: 1) understand general research and development trends in machine learning systems; 2) develop intuition on how to optimize algorithms with hardware in mind; 3) read machine learning system papers critically; 4) write constructive paper reviews; 5) design and execute a research project to address an open research problem in machine learning systems; 6) develop self-learning skills for continuous growth beyond the course.