a) Knowledge: Familiarization with the fundamental principles, algorithms and technology of pattern recognition and statistical machine learning. Exposure to pattern recognition programming using Python, C/C++ and MATLAB. Acquaintance with pattern recognition and statistical machine learning 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, face recognition, action recognition, signal analysis/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 Python, C/C++ and MATLAB.
Course Content (Syllabus)
Clustering (unsupervised learning). Classification/recognition (supervised learning). Decision functions. Classification algorithms utilizing decision functions. Classification based on distance. Classification based on decision theory. Estimation of probability distribution parameters. Semi-supervised learning. Dimensionality reduction (Principal component analysis, Linear discriminant analysis). Graph-based pattern recognition (Analysis of similarity and web graphs, Spectral clustering). Fuzzy logic and pattern recognition. Gaussian Mixture Models. Bayesian networks. Programming assignments in Python, C/C++ and MATLAB.
Random variables, decision functions, clustering, classification based on distance, classification based on decision theory, principal component analysis, linear discriminant analysis, estimation of probability distribution parameters, graph-based pattern recognition, analysis of similarity and web graphs, spectral clustering, fuzzy logic, vector quantization, Bayesian networks, semi-supervised learning.
Course Bibliography (Eudoxus)
1) Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition, 2008
2) Στρίντζης Μ. «Αναγνώριση Προτύπων», Αφοί Κυριακίδη, Θεσσαλονίκη, 2007.
Additional bibliography for study
1) C. Bishop, Pattern Recognition and Machine Learning, Springer 2011
2) Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, Wiley 2007