Pattern Recognition and Machine Learning

Catalog description

This course focuses on the fundamental concepts, theories, and algorithms for pattern recognition and machine learning. Diverse application areas such as optical character recognition, speech recognition, and biometrics are discussed. Topics covered include supervised and unsupervised (clustering) pattern classification algorithms, parametric and non-parametric supervised learning techniques, including Bayesian decision theory, neural networks, support vector machines, nearest neighbor, and genetic algorithms. Instructor approval required to register.