Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Pattern Recognition and Classification:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów
Profil
Moduł
Przedmiot Pattern Recognition and Classification
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Przetwarzania Sygnałów i Inżynierii Multimedialnej
Nauczyciel odpowiedzialny Adam Krzyżak <Adam.Krzyzak@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 4,0 ECTS (formy) 4,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny Grupa obieralna

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
projektyP1 30 2,00,44zaliczenie
wykładyW1 30 2,00,56zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Basic knowledge of Matlab or Mathcad environments
W-2Basic knowledge about programming
W-3Basic knowledge of linear algebra, probability and statistics

Cele przedmiotu

KODCel modułu/przedmiotu
C-1This course is intended to present a unified approach to pattern recognition and classification techniques and their applications in real life problems

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
projekty
T-P-1Students prepare individual project with the requirements given by the teacher.30
30
wykłady
T-W-1Introduction to the subject of pattern recognition.2
T-W-2Bayesian decision theory, discriminant functions for normal class distributions.3
T-W-3parameter estimation and supervised learning, nonparametric techniques (nearest neighbor rules, Parzen kernel rules, tree classifiers).3
T-W-4Adaboost, Breiman random forest, linear discriminant functions.3
T-W-5Fisher linear discriminant and learning including perceptron learning.3
T-W-6LMS algorithms and support vector machines, unsupervised learning and clustering.3
T-W-7Neural networks including multilayer perceptrons and radial basis networks3
T-W-8Elements of machine learning.2
T-W-9Feature selection and dimensionality reduction including PCA.2
T-W-10SOM and Laplacian maps.2
T-W-11Applications of pattern recognition in biometrics including handwriting recognition, face recognition and fingerprint recognition.4
30

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
projekty
A-P-1Participation in classes30
A-P-2Generating projects' reports.20
50
wykłady
A-W-1Participation in classes30
A-W-2Reading relevant literature.20
50

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Traditional lecture. Students prepare individual projects and reports.

Sposoby oceny

KODSposób oceny
S-1Ocena formująca: Written exam (test) / project work

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WE_1-_??_W01
Knowledge of basic pattern recognition algorithms.
C-1T-W-1, T-W-9, T-W-10, T-W-11, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8M-1S-1

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WE_1-_??_U01
Ability to implement some pattern recognition algorithms in chosen environment (e.g. Matlab).
C-1T-W-1, T-W-9, T-W-10, T-W-11, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8M-1S-1

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WE_1-_??_W01
Knowledge of basic pattern recognition algorithms.
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WE_1-_??_U01
Ability to implement some pattern recognition algorithms in chosen environment (e.g. Matlab).
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley, Second Edition, 2001

Literatura dodatkowa

  1. Sergios Theodoridis and Konstantinos Koutroumbas, Pattern Recognition, Fourth Edition, Academic Press, 2008
  2. Brian Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 2008
  3. Simon Haykin, Neural Networks and Learning Machines, hird Edition, Prentice Hall, 2008
  4. Nello Cristianini and John Shawe-Taylor, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, 2000

Treści programowe - projekty

KODTreść programowaGodziny
T-P-1Students prepare individual project with the requirements given by the teacher.30
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Introduction to the subject of pattern recognition.2
T-W-2Bayesian decision theory, discriminant functions for normal class distributions.3
T-W-3parameter estimation and supervised learning, nonparametric techniques (nearest neighbor rules, Parzen kernel rules, tree classifiers).3
T-W-4Adaboost, Breiman random forest, linear discriminant functions.3
T-W-5Fisher linear discriminant and learning including perceptron learning.3
T-W-6LMS algorithms and support vector machines, unsupervised learning and clustering.3
T-W-7Neural networks including multilayer perceptrons and radial basis networks3
T-W-8Elements of machine learning.2
T-W-9Feature selection and dimensionality reduction including PCA.2
T-W-10SOM and Laplacian maps.2
T-W-11Applications of pattern recognition in biometrics including handwriting recognition, face recognition and fingerprint recognition.4
30

Formy aktywności - projekty

KODForma aktywnościGodziny
A-P-1Participation in classes30
A-P-2Generating projects' reports.20
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Participation in classes30
A-W-2Reading relevant literature.20
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WE_1-_??_W01Knowledge of basic pattern recognition algorithms.
Cel przedmiotuC-1This course is intended to present a unified approach to pattern recognition and classification techniques and their applications in real life problems
Treści programoweT-W-1Introduction to the subject of pattern recognition.
T-W-9Feature selection and dimensionality reduction including PCA.
T-W-10SOM and Laplacian maps.
T-W-11Applications of pattern recognition in biometrics including handwriting recognition, face recognition and fingerprint recognition.
T-W-2Bayesian decision theory, discriminant functions for normal class distributions.
T-W-3parameter estimation and supervised learning, nonparametric techniques (nearest neighbor rules, Parzen kernel rules, tree classifiers).
T-W-4Adaboost, Breiman random forest, linear discriminant functions.
T-W-5Fisher linear discriminant and learning including perceptron learning.
T-W-6LMS algorithms and support vector machines, unsupervised learning and clustering.
T-W-7Neural networks including multilayer perceptrons and radial basis networks
T-W-8Elements of machine learning.
Metody nauczaniaM-1Traditional lecture. Students prepare individual projects and reports.
Sposób ocenyS-1Ocena formująca: Written exam (test) / project work
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WE_1-_??_U01Ability to implement some pattern recognition algorithms in chosen environment (e.g. Matlab).
Cel przedmiotuC-1This course is intended to present a unified approach to pattern recognition and classification techniques and their applications in real life problems
Treści programoweT-W-1Introduction to the subject of pattern recognition.
T-W-9Feature selection and dimensionality reduction including PCA.
T-W-10SOM and Laplacian maps.
T-W-11Applications of pattern recognition in biometrics including handwriting recognition, face recognition and fingerprint recognition.
T-W-2Bayesian decision theory, discriminant functions for normal class distributions.
T-W-3parameter estimation and supervised learning, nonparametric techniques (nearest neighbor rules, Parzen kernel rules, tree classifiers).
T-W-4Adaboost, Breiman random forest, linear discriminant functions.
T-W-5Fisher linear discriminant and learning including perceptron learning.
T-W-6LMS algorithms and support vector machines, unsupervised learning and clustering.
T-W-7Neural networks including multilayer perceptrons and radial basis networks
T-W-8Elements of machine learning.
Metody nauczaniaM-1Traditional lecture. Students prepare individual projects and reports.
Sposób ocenyS-1Ocena formująca: Written exam (test) / project work
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student received points in the range of 50-60% of credit questions.
3,5
4,0
4,5
5,0