ABSTRACT This article presents the use of a real life problem to reach a deeper understanding among students of the benefits of principal components analysis. Pattern recognition applied on the 26 letters of the alphabet is a recognizable topic for the students. Moreover it is still verifiable with computer algebra software. By means of well defined exercises the student can be guided in an active way through the learning process.
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nullHecke, T. (2011) Teaching PCA through Letter Recognition. Creative Education, 2, 292-295. doi: 10.4236/ce.2011.23040.
 Dassonville, P., & Hahn, C. (2000). The multimedia tool: A transitional medium beween the mathematician’s culture and the professional’s culture in teaching PCA in a business school. In A. Ahmed, J. Kraemer and H. Williams (Eds.), Cultural diversity in mathematics Education (pp. 125-136). United Kingdom: Horwood
 Hotelling, H. (1935). The most predictable criterion. Journal of Educational Psychology, 26, 139-142. doi:10.1037/h0058165
 Jackson, J. E. (2003). A user’s guide to principal components. New Jersey: Wiley & Sons, Inc..
 Karhunen, K. (1947). über lineare methoden in der wahrscheinli- chkeitsrechnung. Annales Academi? Scientiarum Fennicae Series A1, Mathematica-Physica, 37, 1-79.
 Kastleman, K. (1996). Digital image processing. London: Prentice Hall.
 Loève, M. (1978). Probability theory Vol. II, Graduate Texts in Mathematics 46 (4th ed.). New York, NY: Springer-Verlag.
 Mori, Y., Yamamoto, Y., & Yadohisa, H. (2003). Data-oriented learning system of statistics based on analysis scenario/story (DoLStat). Bulletin of the International Statistical Institute (ISI), 54th Session Proceedings, Volume LX Two Books, Book 2 (pp. 74-77).
 Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neurosicence, 3, 71-86. doi:10.1162/jocn.1922.214.171.124