Pattern Recognition

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Learning Outcomes

Pattern recognition is the scientific field that deals with the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. The course aims to cover the most popular in the literature techniques for pattern recognition, as they are typically employed in a number of practical applications, such as speech and audio recognition, image and video analysis, biometrics and bioinformatics. The course covers the most commonly used classification algorithms, feature selection techniques, data transformation methods, and data clustering.

Course Contents

  • Introduction to Pattern recognition systems
  • Parametric estimation of probability density function (maximum Likelihood estimation, maximum a posteriori
  • Bayesian classifiers and Bayesian Networks
  • k-nearest neighbor
  • Non parametric estimation of probability density function (Parzen windows)
  • Linear classifiers, non linear classifiers. Perceptron algorithm. Multilayer neural networks
  • Unsupervised Pattern recognition – Clustering
  • Feature generation: contour representation and contour tracing, chain code, polygon, signatures, linear transforms, Fourier Transform, regional features, image recognition, bias and variance, texture
  • Feature Selection and Kernels
  • Pattern recognition tools
  • Sergios Theodoridis and Konstantinos Koutroumbas. 2008. Pattern Recognition, Fourth Edition (4th ed.). Academic Press