Neural Machine Learning I

COMP / ELEC / STAT 502, spring 2020

To register for Sp 2020 instructor permission is required. Please see details under the link Required Background, and email me at

This web site is being updated for Sp 2020, changes will occur before classes start.

The Syllabus and Grading Policy may be adjusted to match the available resources for grading and TA support. These adjustments will be made by the end of January, 2020 as the class enrollment settles. The currently posted Syllabus and Grading Policy are based on anticipated demand.

Class meets: TR 2:30 - 3:45pm, TBA
Instructor: Erzsébet Merényi
Office/Phone: DCH 2082, 713-348-3595 
Office hour: Tue 4-5pm or by appointment
Teaching Assistant: Zhenwei Feng
Advising: TBA

Sample Course Outline
Required Background
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Last Updated: February 8, 2020

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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