Monson H. Hayes
In this course, we will be looking at a variety of different topics that lie in the areas of pattern recognition, classification, machine learning, sequential estimation, and adaptive filtering. The common thread through the course will be trying to learn something from data. In some cases, this data may be static, in other cases it may be dynamic, such as time series. Depending on the interest of the class and the time that is available, some of the topics to be covered include perceptron learning, neural networks, theory of generalization and the VC dimension, support vector machines, hidden Markov models, Kalman and particle filters, and importance sampling.
A syllabus for the course may be found here.
Dr. Hayes is a Professor of Electrical and Computer Engeering at George Mason University and Chair of the Department. Dr. Hayes received his Sc.D. in Electrical Engineering and Computer Science from M.I.T. in 1981 and then joined the faculty in the School of Electrical and Computer Engineering at Georgia Tech where he is currently Professor Emeritus. From 2006 until 2011, he was an Assocaite Chair for the School of ECE and Associate Director of Georgia Tech Savannah. In March of 2011, he became a Distinguished Foreign Professor at Chung-Ang University in Seoul, Korea. In the Fall of 2014, Dr. Hayes joined the faculty at George Mason University. Dr. Hayes is the recipient of the 2015 Universidad Carlos III de Madrid Chair of Excellence Award in the area of Deep Learning.
Dr. Hayes has become internationally recognized for his contributions to the field of digital signal processing. He has published more than 180 articles in journals and conference proceedings, and is the author of two textbooks, Statistical Digital Signal Processing and Modeling (Wiley, 1996), and Schaum’s Outline on Digital Signal Processing (McGraw-Hill, 1999). His research interests include DSP algorithms, signal modeling, image and video processing, face recognition, multimedia signal processing, and DSP education. His current projects include face recognition for personalization, lane tracking for driver awareness, hand and gesture recognition for multimedia applications, and equation recognition for handheld devices and the classroom.