Machine Learning Engineering

Catalog description

Machine learning is increasingly driven by advances in the underlying hardware and software systems. This course will focus on the challenges inherent to engineering machine learning systems to be correct, robust, and fast. The course walks through the development of a software library for machine learning from scratch, with each assignment requiring students to build models in their own library. Topics will include: tensor languages and auto-differentiation; model debugging, testing, and visualization; fundamentals of GPUs; compression and low-power inference. Guest lectures will cover current topics from ML engineers.