a) Knowledge: Familiarization with the fundamental principles, algorithms and technology of pattern recognition and statistical machine learning. Exposure to pattern recognition programming using C/C++ and MATLAB. Acquaintance with pattern recognition systems and their application in data analysis.
b) Skills: Setting the foundations for advanced studies on pattern recognition issues and applications in data analysis, e.g. image analysis, shape recognition, signal analy-sis/recognition, analysis of medical information and signals/images, analysis of geographical data, analysis of web data, analysis of financial data.. Acquisition of skills in the use and development of pattern recognition algorithms. Promoting analytical and programming skills. Ability to develop basic pattern recognition applications using C/C++ and MATLAB.
Course Content (Syllabus)
Random variables and vectors. Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on decision theory. Principal component analysis. Linear discriminant analysis. Estimation of probability distribution parameters. Analysis of similarity and web graphs. Syntactical pattern recognition. Vector quantization techniques. Programming assignments in C/C++ and MATLAB.
Course Bibliography (Eudoxus)
Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition, 2008.
Μιχαήλ-Γεράσιμος Στρίντζης, «Αναγνώριση Προτύπων», Αφοί Κυριακίδη, Θεσσαλονίκη, 2007.
Additional bibliography for study
Keinosuke Fukunaga, "Introduction to Statistical Pattern Recognition", Academic Press, 1990.
Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, 2000.