| Area of Mathematics: | Computational and Applied Mathematics (ECAM) |
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| Semester: | 6ο | ||
| Course ID: | 62402 | ||
| Course Type: | Elective | ||
| Teaching hours: | Theory: 3 | Practice: 0 | Laboratory: 0 |
| ECTS : | 5 | ||
| Eclass: | For the course’s material, click here. | ||
| Instructors: | Spiros Georgakopoulos | ||
Pattern Recognition techniques are used to design automated systems that improve their own performance through experience. This course covers the methodologies, technologies and algorithms of Pattern Recognition from a variety of perspectives. Topics include: clustering and classification, Pattern Recognition systems, Bayesian classifiers, k-nearest neighbour, parametric estimation of probability density function (maximum Likelihood estimation, maximum a posteriori), non-parametric estimation of probability density function (Parzen windows), linear classifiers, nonlinear classifiers, Perceptron algorithm, multilayer neural networks, feature generation, and real world problem solving.
The algorithms and methods will be conducted with Programming Language Python.
- Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer New York, ISBN 978-0-387-31073-2
- Pattern Classification, 2nd Edition, Richard O. Duda, Peter E. Hart, David G. Stork, ISBN: 978-0-471-05669-0
