EE104: Introduction to Machine Learning

Stanford University, Spring Quarter 2018

Course offerings

  • This is the website for the Spring 2018 version of the course, which was the first time this course was offered.

  • This course will next be taught by Sanjay Lall in Spring 2019.


Professor Sanjay Lall and Professor Stephen Boyd

Course description

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. In this initial offering, enrollment is limited to 50 students. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units.

Prerequisites: EE 103; EE 178 or CS 109; CS106A or equivalent.


There are no required or optional textbooks. Complete notes will be available online.

A useful reference will be the EE103 course textbook, Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares. The book is now complete.