ECE544NA is a permanent special-topics course in pattern recognition. Content varies every year, but usually includes error metrics (e.g., information-theoretic and perceptron-based) and optimization (e.g., neural network, Bayesian, stochastic, and convex programming techniques) for the supervised, semi-supervised, and unsupervised estimation of probability densities, feature selection, regression and classification. In fall 2013, ECE 544NA will cover the material usually included in this course, but with a particular emphasis on neural networks. This emphasis on neural networks is prompted by the dramatic success of pre-trained deep belief networks, recently, in many pattern recognition applications.