Neural Machine Learning II

COMP / ELEC / STAT 602, fall 2020


Instructor: Erzsébet Merényi
Class meets: TTH 4:50 - 6:10pm, via Zoom
(URL will be sent separately)
Office/Phone: DH 2082, 713-348-3595 
email is preferred during pandemic
Office hours: by appointment via Zoom
Assistant: N/A
email: erzsebet@rice.edu
Sample Course Topics
Syllabus
Prerequisites
Course Flyer
Disability Allowances

Last Updated: September 15, 2020


Welcome to biologically inspired neural information processing!

Short course description: Advanced topics in Artificial Neural Network theories, with a focus on learning high-dimensional complex manifolds with neural maps (Self-Organizing Maps and variants, Learning Vector Quantization variants, both unsupervised and supervised paradigms). Application to data mining, clustering, classification, dimension reduction, sparse representation. Comparison with "gold standards" on data of various complexities. Examples through image and signal processing, bioinformatics, brain mapping from fMRI, environmental mapping from spectral imagery. The course will be a mix of lectures and seminar style discussions with active student participation, based on recent research publications. Strong coding skills in MATLAB, R, or C are assumed. Students may also have access to research software environment to do simulation experiments.  

Want a small glimpse in layman's terms?