Foundations of Machine Learning
A broad introduction to the foundations of Machine Learning (ML), as well as hands-on experience in applying ML algorithms to real-world data sets. Topics include various techniques in supervised and unsupervised learning, as well as applications to computer vision, data mining, and speech recognition.
Topics
- various techniques in supervised and unsupervised learning
- as well as applications to computer vision
- data mining
- and speech recognition
Topics explicitly listed in official catalogue description
Prerequisites & course information
Prerequisite: 1 course with a minimum grade of C- from ( ENEE324 , STAT400 ); and 1 course with a minimum grade of C- from ( ENEE150 , CMSC216 ); and permission of ENGR-Electrical & Computer Engineering department. Restriction: Permission of ENGR-Electrical & Computer Engineering department. And must be in one of the following programs (Engineering: Electrical; Engineering: Computer) ; or must be in the ECE Department's Machine Learning notation program. Credit Only Granted for: ENEE436 , ENEE439M, or CMSC422 . Formerly: ENEE439M.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Foundations of Machine Learning · 3 Credits