Introduction to Machine Learning Systems

Semester plan · Volume I

Overview

Students study how data pipelines, training, optimization, and deployment work on a single machine, and they build a small deep learning framework of their own. The course assumes Python, linear algebra, basic calculus, and introductory probability.

Syllabus and schedule

The semester syllabus lists one Volume I chapter for each of the 16 weeks. Each offering sets its own dates and course policies.

Materials

Resource	Use
Volume I	The weekly readings.
Lecture slides	One deck per chapter, with speaker notes and in-class exercises.
Interactive labs	Browser-based experiments that pair with the readings.
TinyTorch	The framework students build from scratch, one module at a time.

For instructors

The course can also serve as the first half of a two-semester sequence that continues with the scaling course.

Offerings

The course directory lists courses at other institutions that use MLSysBook.