Pattern Recognition


Introduction to the theory and algorithms of statistical pattern recognition with applications to recognition of sounds (e.g. speech, music), visual objects, audio-visual events, and other spatio-temporal sensory or symbolic data. Bayesian decision and estimation theory (Maximum Likelihood, Maximum-A-Posteriori). Nearest neighbor decision rule. Methods for clustering (e.g. k-means) and unsupervised learning. Decision trees. Methods for feature transformation and selection in pattern space, and dimensionality reduction: principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA). Methods for linear and nonlinear regression. Pattern classification methods with linear discriminant machines: Perceptrons και Support Vector Machines. Hidden Markov models (HMMs), Gaussian Mixture models (GMMs), Expectation-Maximization algorithm, Viterbi algorithm. Dynamic Bayesian nets. Probabilistic graphical models. Deep learning methods: Deep, Convolutional, Recursive Neural Nets (DNNs CNNs, RNNs). Analytic and laboratory exercises.


Petros Maragos Διδάσκων (Instructor)

Alexandros Potamianos Διδάσκων (Instructor)

Nancy Zlatintsi



  • [KS] Γ. Καραγιάννης και Γ. Σταϊνχάουερ,
    Αναγνώριση Προτύπων και Μάθηση Μηχανών,
    ΕΜΠ, 2001 (βιβλίο).
  • Σ. Θεοδωρίδης και Κ. Κουτρουμπάς,
    Αναγνώριση Προτύπων,
    Ιατρικές εκδόσεις Π.Χ. Πασχαλίδης, 2011.
  • Π. Μαραγκός,
    Συμπληρωματικές Σημειώσεις Αναγνώρισης Προτύπων και Φωνής,

Συμπληρωματικές Σημειώσεις

Μάθηση Μηχανών και Αναγνώριση Προτύπων  
Συμπληρωματικές Σημειώσεις
Pattern RecognitionΑναγνώριση Προτύπων
Linear Algebra NotesΓραμμική Άλγεβρα
Pattern Recognition SlidesΔιαφάνειες Αναγνώριση Προτύπων
New Slides 2017



  • [DHS] R. O. Duda, P.E. Hart and D.G. Stork,
    Pattern Classification,
    Wiley, 2001.
  • [Bishop] C. M. Bishop,
    Pattern Recognition and Machine Learning,
    Springer, 2006.
  • [Goodfellow-et-al], I. Goodfellow, Y. Bengio and A. Courville,
    Deep Learning,
    MIT Press,