NEURAL MACHINE LEARNING I
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: COMP 502 , ELEC 502 .
Prerequisites & course information
Short Title: NEURAL MACHINE LEARNING I Department: Statistics Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 3 Restrictions: Enrollment is limited to Graduate level students. Course Level: Graduate Course URL: www.ece.rice.edu/~erzsebet/ANNcourse.html
Sources
- Course catalogue ↗2026–2027