Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Artificial Neural Networks and their Application in System Modeling:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów —
Profil
Moduł —
Przedmiot Artificial Neural Networks and their Application in System Modeling
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Metod Sztucznej Inteligencji i Matematyki Stosowanej
Nauczyciel odpowiedzialny Marcin Pluciński <Marcin.Plucinski@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 3,0 ECTS (formy) 3,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny — Grupa obieralna —

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW1 15 1,00,30zaliczenie
laboratoriaL1 15 2,00,70zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Basics of algebra and mathematical analysis.
W-2Basics of computer science.

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Extending of the knowledge about artificial neural networks, their construction, operation and learning techniques.
C-2Gaining practical skills in the application of neural networks to solve real tasks of modeling and classification.
C-3Familiarization with the software that could be used in tasks of modeling and classification using neural networks.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
laboratoria
T-L-1Application of simple perceptron neural network to solve classification tasks.2
T-L-2Application of feed-forward multilayer neural networks to solve complex real tasks of classification.2
T-L-3Application of feed-forward multilayer neural network in modeling (real technical, economic and medical problems).2
T-L-4Applications of RBF neural networks in modeling of technical and economic problems.2
T-L-5Application of unsupervised learning networks to the data clustering problem.2
T-L-6Hopfield network - application to the pattern recognition problem.2
T-L-7Final work.3
15
wykłady
T-W-1The introduction to neural networks. Feed-forward neural networks. The structure and operation of the artificial neuron.2
T-W-2Simple Perceptron network - structure and learning methods. Example of learning and action of the network. Selected applications of the Perceptron network.2
T-W-3Feed-forward multilayer neural networks. Network learning methods - backpropagation. Examples of learning and operation of the network. Selected network applications. Selection of the optimal network architecture.3
T-W-4Neural networks with radial basis function - RBF neural networks. Structure and learning methods. Examples of applications. Probabilistic neural networks.3
T-W-5Self-organizing networks - unsupervised learning algorithms. The strucrure and operation of networks. Kohonen's network and learning algorithm. Examples of applications of self-organizing networks.2
T-W-6Recursive networks - Hopfield network, Hamming network. Construction, operation, learning methods. Examples of network applications.2
T-W-7Evaluation of knowledge.1
15

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
laboratoria
A-L-1Participation in labs.15
A-L-2Developement of programs and preparation of reports on the lab activity.45
60
wykłady
A-W-1Participation in lectures and evaluation.15
A-W-2Self preparing to final evaluation.12
A-W-3Realization of homework.3
30

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Lecture with presentation.
M-2Labs - self-realization of tasks with the application of neural networks. Work will be done using Matlab ANN Toolbox and self-developed software.

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Lecture: written test.
S-2Ocena formująca: Laboratory: evaluation of tasks carried out during the classes.
S-3Ocena formująca: Laboratory: evaluation of reports.
S-4Ocena podsumowująca: Laboratory: evaluation of final 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-WI_1-_??_W01
The student knows the types of artificial neural networks, their structure, operation and ways of learning.		——C-1T-W-1, T-W-4, T-W-3, T-W-6, T-W-2, T-W-5M-1S-1
WM-WI_1-_??_W02
The student knows practical applications of specific types of artificial neural networks.		——C-1T-W-1, T-W-4, T-W-3, T-W-6, T-W-2, T-W-5M-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-WI_1-_??_U01
The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks.		——C-2, C-3T-L-7, T-L-4, T-L-1, T-L-6, T-L-5, T-L-3, T-L-2M-2S-3, S-2, S-4

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WI_1-_??_W01
The student knows the types of artificial neural networks, their structure, operation and ways of learning.		2,0
3,0The student knows the types of artificial neural networks, their structure, operation and ways of learning at the basic level.
3,5
4,0
4,5
5,0
WM-WI_1-_??_W02
The student knows practical applications of specific types of artificial neural networks.		2,0
3,0The student knows practical applications of specific types of artificial neural networks at the basic level.
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WI_1-_??_U01
The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks.		2,0
3,0The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks at the basic level.
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. David Kriesel, A Brief Introduction to Neural Networks, 2012
  2. James A. Freeman, David M. Skapura, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley Publishing Company, 2005

Treści programowe - laboratoria

KODTreść programowaGodziny
T-L-1Application of simple perceptron neural network to solve classification tasks.2
T-L-2Application of feed-forward multilayer neural networks to solve complex real tasks of classification.2
T-L-3Application of feed-forward multilayer neural network in modeling (real technical, economic and medical problems).2
T-L-4Applications of RBF neural networks in modeling of technical and economic problems.2
T-L-5Application of unsupervised learning networks to the data clustering problem.2
T-L-6Hopfield network - application to the pattern recognition problem.2
T-L-7Final work.3
15

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1The introduction to neural networks. Feed-forward neural networks. The structure and operation of the artificial neuron.2
T-W-2Simple Perceptron network - structure and learning methods. Example of learning and action of the network. Selected applications of the Perceptron network.2
T-W-3Feed-forward multilayer neural networks. Network learning methods - backpropagation. Examples of learning and operation of the network. Selected network applications. Selection of the optimal network architecture.3
T-W-4Neural networks with radial basis function - RBF neural networks. Structure and learning methods. Examples of applications. Probabilistic neural networks.3
T-W-5Self-organizing networks - unsupervised learning algorithms. The strucrure and operation of networks. Kohonen's network and learning algorithm. Examples of applications of self-organizing networks.2
T-W-6Recursive networks - Hopfield network, Hamming network. Construction, operation, learning methods. Examples of network applications.2
T-W-7Evaluation of knowledge.1
15

Formy aktywności - laboratoria

KODForma aktywnościGodziny
A-L-1Participation in labs.15
A-L-2Developement of programs and preparation of reports on the lab activity.45
60
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Participation in lectures and evaluation.15
A-W-2Self preparing to final evaluation.12
A-W-3Realization of homework.3
30
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_W01The student knows the types of artificial neural networks, their structure, operation and ways of learning.
Cel przedmiotuC-1Extending of the knowledge about artificial neural networks, their construction, operation and learning techniques.
Treści programoweT-W-1The introduction to neural networks. Feed-forward neural networks. The structure and operation of the artificial neuron.
T-W-4Neural networks with radial basis function - RBF neural networks. Structure and learning methods. Examples of applications. Probabilistic neural networks.
T-W-3Feed-forward multilayer neural networks. Network learning methods - backpropagation. Examples of learning and operation of the network. Selected network applications. Selection of the optimal network architecture.
T-W-6Recursive networks - Hopfield network, Hamming network. Construction, operation, learning methods. Examples of network applications.
T-W-2Simple Perceptron network - structure and learning methods. Example of learning and action of the network. Selected applications of the Perceptron network.
T-W-5Self-organizing networks - unsupervised learning algorithms. The strucrure and operation of networks. Kohonen's network and learning algorithm. Examples of applications of self-organizing networks.
Metody nauczaniaM-1Lecture with presentation.
Sposób ocenyS-1Ocena podsumowująca: Lecture: written test.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student knows the types of artificial neural networks, their structure, operation and ways of learning at the basic level.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_W02The student knows practical applications of specific types of artificial neural networks.
Cel przedmiotuC-1Extending of the knowledge about artificial neural networks, their construction, operation and learning techniques.
Treści programoweT-W-1The introduction to neural networks. Feed-forward neural networks. The structure and operation of the artificial neuron.
T-W-4Neural networks with radial basis function - RBF neural networks. Structure and learning methods. Examples of applications. Probabilistic neural networks.
T-W-3Feed-forward multilayer neural networks. Network learning methods - backpropagation. Examples of learning and operation of the network. Selected network applications. Selection of the optimal network architecture.
T-W-6Recursive networks - Hopfield network, Hamming network. Construction, operation, learning methods. Examples of network applications.
T-W-2Simple Perceptron network - structure and learning methods. Example of learning and action of the network. Selected applications of the Perceptron network.
T-W-5Self-organizing networks - unsupervised learning algorithms. The strucrure and operation of networks. Kohonen's network and learning algorithm. Examples of applications of self-organizing networks.
Metody nauczaniaM-1Lecture with presentation.
Sposób ocenyS-1Ocena podsumowująca: Lecture: written test.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student knows practical applications of specific types of artificial neural networks at the basic level.
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WI_1-_??_U01The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks.
Cel przedmiotuC-2Gaining practical skills in the application of neural networks to solve real tasks of modeling and classification.
C-3Familiarization with the software that could be used in tasks of modeling and classification using neural networks.
Treści programoweT-L-7Final work.
T-L-4Applications of RBF neural networks in modeling of technical and economic problems.
T-L-1Application of simple perceptron neural network to solve classification tasks.
T-L-6Hopfield network - application to the pattern recognition problem.
T-L-5Application of unsupervised learning networks to the data clustering problem.
T-L-3Application of feed-forward multilayer neural network in modeling (real technical, economic and medical problems).
T-L-2Application of feed-forward multilayer neural networks to solve complex real tasks of classification.
Metody nauczaniaM-2Labs - self-realization of tasks with the application of neural networks. Work will be done using Matlab ANN Toolbox and self-developed software.
Sposób ocenyS-3Ocena formująca: Laboratory: evaluation of reports.
S-2Ocena formująca: Laboratory: evaluation of tasks carried out during the classes.
S-4Ocena podsumowująca: Laboratory: evaluation of final work.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The student has the ability to solve practical problems (economic, technical and other) using artificial neural networks at the basic level.
3,5
4,0
4,5
5,0