EE104/CME107: Introduction to Machine Learning

Stanford University, Spring Quarter 2020

EE104 is the same as CME107.


Professors Sanjay Lall and 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. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units.

Prerequisites: EE103/CME103; EE178 or CS109; CS106A or equivalent.


  • Tuesdays and Thursdays, 9:00–10:20am


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

A useful reference will be the EE103/CME103 course textbook, Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares.


  • There will be one midterm exam and one final exam.

  • The formats of both exams are to be determined.

  • The final exam's official date and time is Monday, June 8, 2020, from 8:30-11:30am.