Units: 4
Hours: Lecture, 3 hours; discussion, 1 hour
Catalog page 480
Prerequisites: MATH 010A with a grade of C- or better; MATH 031 with a grade of C- or better; or equivalent; or consent of instructor
Description: Introduction to mathematical and computational concepts in machine learning methods with emphasis on developing new machine learning algorithms. Topics include linear algebra and vector calculus in application to supervised learning, regression, classification, unsupervised learning, clustering, and dimensionality reduction. Topics also include optimization and probability theory used in machine learning algorithms.