NEURAL MACHINE LEARNING I

Credit Hours: 3 · Graduate Course URL: www.ece.rice.edu/~erzsebet/ANNcourse.html

Review of major neural machine learning (Artificial Neural Network) paradigms. Analytical discussion of supervised and unsupervised neural learning algorithms and their relation to information theoretical methods. Practical applications to data analysis such as pattern recognition, clustering, classification, function approximation/regression, non-linear PCA, projection pursuit, independent component analysis, with lots of examples from image and digital processings. Details are posted at www.ece.rice.edu/~erzsebet/ANNcourse.html. Cross-list: ELEC 502 , STAT 502 . Recommended Prerequisite(s): ELEC 430 and ELEC 431 or equivalent or permission of instructor.