NEURAL MACHINE LEARNING AND DATA MINING II
Advanced topics in ANN theories, with a focus on learning high-dimensional complex manifolds with neural maps (Self-Organizing Maps, Learning Vector Quantizers and variants). Application to data mining, clustering, classification, dimension reduction, sparse representation. The course will be a mix of lectures and seminar discussions with active student participation, based on most recent research publications. Students will have access to professional software environment to implement theories. Cross-list: COMP 602 , ELEC 602 . Repeatable for Credit.
Prerequisites & course information
Short Title: NEURAL MACHINE LEARNING II Department: Statistics Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Graduate level students. Course Level: Graduate Prerequisite(s): ELEC 502 or COMP 502 or STAT 502 Course URL: www.ece.rice.edu/~erzsebet/NMLcourseII.html
Sources
- Course catalogue ↗2026–2027