Welcome to EE104/CME107, Spring 2024!
Lectures will be in room 420-041, Tuesdays and Thursdays, 10:30–11:50 AM.
First class: Tuesday April 2. Last class: Tuesday June 4.
This course was developed by Professor Sanjay Lall and Professor Stephen Boyd. This year it will be taught by Stephen Boyd.
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: linear algebra at the level of ENGR 108, CME 104, MATH 104, or MATH 113; CS106a or similar introductory experience with programming. A probability course such as CME 106, EE 178, or CS 109 is required, but is a co-requisite; it may be taken at the same time as EE 104.
There are no required or optional textbooks. Complete notes will be available online.
A useful reference will be the ENGR108 course textbook, Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares.