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MAT 256: Visual Design Through Algorithms

MAT 256: Visual Design Through Algorithms. Professor George Legrady, legrady@arts.ucsb.edu Professor Jerry Gibson, gibson@mat.ucsb.edu Winter 2006, e-studio, Art 2220 Tuesday 1pm-2pm, Jerry Gibson Lecture Thursday 12pm-1pm, George Legrady Lecture Thursday 12pm-1pm, Lab

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MAT 256: Visual Design Through Algorithms

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  1. MAT 256: Visual Design Through Algorithms • Professor George Legrady, legrady@arts.ucsb.edu • Professor Jerry Gibson, gibson@mat.ucsb.edu • Winter 2006, e-studio, Art 2220 • Tuesday 1pm-2pm, Jerry Gibson Lecture • Thursday 12pm-1pm, George Legrady Lecture • Thursday 12pm-1pm, Lab • TA Will Wolcott, wolcott@umail.ucsb.edu • Course Web Site : http://www.mat.ucsb.edu/~g.legrady/

  2. What is MAT 256? Course Definition • An interdisciplinary team-taught course with goals to foster engineering-level research in conjunction with the experimental approach of the visual arts. • Course consists of weekly lectures by both engineering and arts faculty

  3. Course Goals • Engineering component: understanding of the scientific premise of the algorithms • Artistic component: to aim for aesthetic richness and visual/conceptual innovation • The course is meant to function as a resource and reference for future research

  4. Student Workload • 3 multimedia projects that explore mathematical visual processes, interactivity and visual perception: 1) Visual FFT, 2) Randomness, 3) Final Project • Lecture and lab attendance • Directed reports on readings posted at course mailing list • Projects to be documented online

  5. Interdisciplinarity • Students will be paired based on different backgrounds (artist, engineer, social science, etc.) • Goal is to hybridize, exchange problem solving methods, ideas, empirical results • We will study how we work in teams, how we share knowledge, and arrive at results • Comparison arts and engineering research methods

  6. Engineering Course Content • Filtering, sampling, and reconstruction • Fourier Series, Orthogonal Series, and Approximations, • Randomness, Markov Chains • Noise & Information • Convolution and System Response • Transfer functions • Discrete Transforms

  7. Arts Course Content • Discussion of what is art research • overview of computational aesthetics • a review of the function of images • principles of visual communication • chance, noise, and information • art examples of the implementation of algorithmic processes in contemporary arts and architecture • algorithmic visual design and iterative processes as a form of image production experimentation

  8. Student Projects • Projects will be experimental but show an understanding of lecture material covered • Flexibility in content, technologies, materials • Emphasis on the visual, and possibly interactivity • Driven by research methods, structures, systems, etc.

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