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Georgetown University


Detailed Course Information


Fall 2017
Oct 19, 2017
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MATH 242 - Appld Linr Algebra &Graph Thry
This course will focus on computational applications of linear algebra and graph theory to diverse problems such as encryption and decryption, audio and image compression, Google's PageRank algorithm, the Netflix Contest, and social network analysis. The material covered will be beneficial to students interested in learning applications of linear algebra and graph theory to problems of increasing importance in technology settings, such as the emerging field of data science. Experience with scientific computing, such as Matlab or Octave, or Python's numpy and scipy libraries will be useful, but is not required.

Textbook: Coding the Matrix, by Philip N. Klein, Newtonian Press, 2013.

3.000 Credit hours
3.000 Lecture hours

Levels: Undergraduate
Schedule Types: Lecture

Mathematics Department

Course Attributes:
Mean Grade is Calculated

MATH 150

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Release: 8.7.2