http://hdl.handle.net/1721.1/33997 Media Arts and Sciences (MAS) http://hdl.handle.net/1721.1/49532 MAS.160 Signals, Systems, and Information for Media Technology, Fall 2001 Massachusetts Institute of Technology. Media Laboratory. Fundamentals of signals, systems, and information theory with emphasis on modeling both the audio/visual message and the human recipient. Linear systems, difference equations, Z-transforms, sampling and sampling rate conversion, convolution, filtering, modulation, Fourier analysis, entropy, noise, Shannon's fundamental theorems. Additional topics may include data compression, filter design, and feature detection. Meets with graduate subject MAS.510, MAS.511 but assignments differ. http://hdl.handle.net/1721.1/49531 MAS.110 Fundamentals of Computational Media Design, Spring 2003 Maeda, John Introduces principles of analysis and synthesis in the computational medium. Expressive examples that illustrate the intersection of computation with the traditional arts are developed on a weekly basis. Hands-on design exercises are continually framed and examined in the larger context of contemporary digital art. Limited enrollment. http://hdl.handle.net/1721.1/49510 MAS.630 Affective Computing, Spring 2002 Picard, Rosalind Explores computing that relates to, arises from, or deliberately influences emotion. Topics include the interaction of emotion with cognition and perception, the role of emotion in human-computer interaction, the communication of human emotion via face, voice, physiology, and behavior, construction of computers that can recognize and respond appropriately to human emotional expressions, the development of computers that "have" emotion, and other areas of current research interest. Weekly reading, discussion, and a term project required. http://hdl.handle.net/1721.1/41935 MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000 Massachusetts Institute of Technology. Media Laboratory. Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.