COMP / ELEC / STAT 502, spring 2020
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.
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.