Welcome to the Home Page for the CAU course on Linear Algebra. On this site you will find a variety of useful resources, including lecture notes, problem sets, puzzles, applications, demos that illustrate many of the concepts and applications covered in the course, and links to important resources outside this web page.
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This course will give the student an introduction to Linear Algebra and illustrate its importance through a number of interesting and diverse applictions such as coding theory, cryptography, tomography, vector graphics, games, economics, genetics, image compression, Markov chains, and eigenfaces. The course will teach the student to be proficient in solving systems of linear equations and working with vectors and matrices. Equally important, the course will teach the student how to conceptualize systems of linear systems in terms of vector spaces.
The objectives of the course will be
There are NO prerequisites for this course except:
Dr. Hayes is a Professor of Electrical Engineering. 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 a Full Professor. 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 while still maintaing his association with Georgia Tech through his research and his students.
Â 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 Schaumâ€™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.