Neural Machine Learning I

COMP / ELEC / STAT 502, spring 2018

The Syllabus and Grading Policy will have to be adjusted due to large class size. These adjustments will be made by the end of January, 2018 as the class enrollment settles, to match the available resources for grading and TA support. The currently posted Syllabus and Grading Policy are based on anticipated demand.

Class meets: TR 2:30 - 3:45pm, DCH 1064
Instructor: Erzsébet Merényi
email: erzsebet@rice.edu
Office/Phone: DCH 204082, 713-348-3595 
Office hour: Tuesdays 4-5pm or by appointment
Teaching Assistant / grader: Zhengjia Wang
Advising: Wed 5 - 7 pm DCH 2094, or by appointment
Teaching Assistant / grader: Ragib Mostofa
Advising: Mo 5 - 7 pm DCH 1044, or by appointment
Sample Course Outline
Syllabus
Prerequisites
Course Flyer
Disability Allowances





Last Updated: April 6, 2018
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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