Neural Machine Learning I

COMP / ELEC / STAT 502, spring 2016


Class meets: TTH 1:00 - 2:15pm, DCH 1046
Instructor: Erzsébet Merényi
email: erzsebet@rice.edu
Office/Phone: DH 2040, 713-348-3595 
Office hour: TBA or by appointment
Teaching Assistant: Joshua Taylor, jtay@rice.edu
Advising: TBA, or by appointment
Grader: Jose Vera-Garza, jc@losvg.com
Advising: TBA, or by appointment
Grader: Dalu Yang, dy11@rice.edu
Advising: TBA, or by appointment
(unavailable between Feb 3 - 23)

Sample Course Outline
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Last Updated: May 12, 2016


Welcome to biologically inspired neural information processing!
Short course description: Review of major Artificial Neural Network paradigms. Analytical discussion of supervised and unsupervised learning. Emphasis on state-of-the-art Hebbian (biologically most plausible) learning paradigms and their relation to information theoretical methods. Applications to data analysis such as pattern recognition, clustering (information discovery), classification, non-linear PCA, independent component analysis, with lots of examples from image and signal processing and other areas. 

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