COMP / ELEC / STAT 502, spring 2020
This web site is being updated for implementation of Covid-19 measures in the rest of Sp 2020, changes may occur relative to the posted Syllabus; and relative to previously announced plans. Updates will post in both the Course Schedule below and in Canvas.
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.