Neural Machine Learning I

COMP / ELEC / STAT 502, spring 2017

Jan 31, 2017: The Syllabus and Grading Policy has been adjusted due to large class size and unavailability of sufficient resources for grading and TA support. Please review the revised Syllabus and Grading Policy.

Class meets: TTH 2:30 - 3:45pm, DCH 1042
Instructor: Erzsébet Merényi
Office/Phone: DH 2040, 713-348-3595 
Office hour: Tuesdays 4-5pm or by appointment
Teaching Assistant: TBA
Advising: TBA, or by appointment
Assistant: Paul (Jesse) Hellemn,
Advising: TBA, or by appointment
Assistant: Kentaro Hoffman,
Advising: TBA, or by appointment
more TBA

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Last Updated: April 13, 2017

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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