Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Wydział Elektryczny - Teleinformatyka (S1)

Sylabus przedmiotu Artificial Intelligence:

Informacje podstawowe

Kierunek studiów Teleinformatyka
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta inżynier
Obszary studiów charakterystyki PRK, kompetencje inżynierskie PRK
Profil ogólnoakademicki
Moduł
Przedmiot Artificial Intelligence
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Przetwarzania Sygnałów i Inżynierii Multimedialnej
Nauczyciel odpowiedzialny Adam Krzyżak <Adam.Krzyzak@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 2,0 ECTS (formy) 2,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny 15 Grupa obieralna 1

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW6 15 1,00,62zaliczenie
projektyP6 15 1,00,38zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Knowledge of mathematics at an engineering level

Cele przedmiotu

KODCel modułu/przedmiotu
C-1To familiarize the student with methods of pattern recognition, cluster analysis and dimensionality reduction
C-2Introducing the student to the possibility of using learning systems under supervision and without supervision
C-3Developing the student's ability to use basic adaptive rules in the problem of pattern classification

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
projekty
T-P-1Application of a selected method of a supervised statistical learning system in the problem of pattern recognition7
T-P-2Implementation of the selected neural network training method in a programming environment8
15
wykłady
T-W-1Introduction to statistics2
T-W-2Probabilistic classification methods2
T-W-3Regression methods1
T-W-4ROC curves1
T-W-5Support vector machines1
T-W-6Nearest neighbour method1
T-W-7Neural networks3
T-W-8Decomposition of multi-class problems1
T-W-9Boosting classifiers1
T-W-10Principal component analysis1
T-W-11Clustering and correspondence analysis. Passing the lectures.1
15

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
projekty
A-P-1participation in classes15
A-P-2individual work on the project8
A-P-3Consultancy2
25
wykłady
A-W-1participation in lectures15
A-W-2literature studies5
A-W-3preparation for course passing5
25

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1informative lectures
M-2problem-based lectures
M-3lectures with the use of a computer
M-4project method

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Based on written and oral assessment
S-2Ocena podsumowująca: Based on the presentation of work results and as-built documentation
S-3Ocena formująca: Didactic discussion
S-4Ocena formująca: Based on observations of group work

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaOdniesienie do efektów uczenia się prowadzących do uzyskania tytułu zawodowego inżynieraCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
TI_1A_C32.2_W01
Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction with multivariate statistical methods
TI_1A_W04C-2, C-1, C-3T-W-10, T-W-1, T-W-5, T-W-3, T-W-4, T-W-2, T-W-11, T-W-6M-3, M-2, M-1S-1, S-3
TI_1A_C32.2_W02
Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction by means of neural networks
TI_1A_W04C-3, C-1, C-2T-W-7, T-W-9, T-W-8M-1, M-2, M-3S-3, S-1

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaOdniesienie do efektów uczenia się prowadzących do uzyskania tytułu zawodowego inżynieraCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
TI_1A_C32.2_U01
Student is able to use adaptive rules in pattern classification using statistical techniques
TI_1A_U09C-3T-P-1M-4S-4, S-2
TI_1A_C32.2_U02
Student is able to use adaptive rules in pattern classification using neural networks
TI_1A_U09C-3T-P-2M-4S-4, S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
TI_1A_C32.2_W01
Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction with multivariate statistical methods
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of the use of statistical methods
3,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 50-60% of the total score for final questions
3,5Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 61-70% of the total score for final questions
4,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 71-80% of the total score for final questions
4,5Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 81-90% of the total score for final questions
5,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 91-100% of the total score for final questions
TI_1A_C32.2_W02
Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction by means of neural networks
2,0Does not meet the requirements for obtaining a satisfactory grade by obtaining less than 50% of the total score on the exam questions in the field of neural networks
3,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 50-60% of the total score for final questions
3,5Has knowledge in the field of neural networks, documented by obtaining a score in the range of 61-70% of the total score for final questions
4,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 71-80% of the total score for final questions
4,5Has knowledge in the field of neural networks, documented by obtaining a score in the range of 81-90% of the total score for final questions
5,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 91-100% of the total score for final questions

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
TI_1A_C32.2_U01
Student is able to use adaptive rules in pattern classification using statistical techniques
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of the use of statistical methods
3,0Is able to use statistical methods for pattern classification, obtaining a score in the range of 50-60% in the assessment of a design task in this area
3,5Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 61-70% in the assessment of a design task in this area
4,0Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 71-80% in the assessment of a design task in this area
4,5Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 81-90% in the assessment of a design task in this area
5,0Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 91-100% in the assessment of a design task in this area
TI_1A_C32.2_U02
Student is able to use adaptive rules in pattern classification using neural networks
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of using a neural network for pattern classification
3,0Can use a neural network for pattern classification, obtaining a score in the range of 50-60% in the assessment of a design task in this area
3,5Can use a neural network for pattern classification, obtaining a score in the range of 61-70% in the assessment of a design task in this area
4,0Can use a neural network for pattern classification, obtaining a score in the range of 71-80% in the assessment of a design task in this area
4,5Can use a neural network for pattern classification, obtaining a score in the range of 81-90% in the assessment of a design task in this area
5,0Can use a neural network for pattern classification, obtaining a score in the range of 91-100% in the assessment of a design task in this area

Literatura podstawowa

  1. Russel S.J., Norvig P., Artificial Intelligence. A Modern Approach, Pearson, Hoboken, USA, 2024, Fourth
  2. Duda R. O., Hart P. E. and Stork D. G., Pattern Classification, John Wiley & Sons, New York, 2001, Second
  3. Luger G. F., Principles of Artificial Intelligence, Springer Nature, Heidelberg, 2024

Literatura dodatkowa

  1. Krzyśko M., Wołyński W., Górecki T., Skorzybut M., Systemy Uczące Się, Wydawnictwo Naukowo-Techniczne, Warszawa, 2008
  2. Osowski S., Metody Sztucznej Inteligencji, Oficyna Wydawnicz PW, Warszawa, 2000
  3. Rutkowski L., Metody i Techniki Sztucznej Inteligencji, PWN, Warszawa, 2023, Drugie

Treści programowe - projekty

KODTreść programowaGodziny
T-P-1Application of a selected method of a supervised statistical learning system in the problem of pattern recognition7
T-P-2Implementation of the selected neural network training method in a programming environment8
15

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Introduction to statistics2
T-W-2Probabilistic classification methods2
T-W-3Regression methods1
T-W-4ROC curves1
T-W-5Support vector machines1
T-W-6Nearest neighbour method1
T-W-7Neural networks3
T-W-8Decomposition of multi-class problems1
T-W-9Boosting classifiers1
T-W-10Principal component analysis1
T-W-11Clustering and correspondence analysis. Passing the lectures.1
15

Formy aktywności - projekty

KODForma aktywnościGodziny
A-P-1participation in classes15
A-P-2individual work on the project8
A-P-3Consultancy2
25
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1participation in lectures15
A-W-2literature studies5
A-W-3preparation for course passing5
25
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięTI_1A_C32.2_W01Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction with multivariate statistical methods
Odniesienie do efektów kształcenia dla kierunku studiówTI_1A_W04Ma szczegółową wiedzę związaną z wybranymi zagadnieniami w obszarze teleinformatyki.
Cel przedmiotuC-2Introducing the student to the possibility of using learning systems under supervision and without supervision
C-1To familiarize the student with methods of pattern recognition, cluster analysis and dimensionality reduction
C-3Developing the student's ability to use basic adaptive rules in the problem of pattern classification
Treści programoweT-W-10Principal component analysis
T-W-1Introduction to statistics
T-W-5Support vector machines
T-W-3Regression methods
T-W-4ROC curves
T-W-2Probabilistic classification methods
T-W-11Clustering and correspondence analysis. Passing the lectures.
T-W-6Nearest neighbour method
Metody nauczaniaM-3lectures with the use of a computer
M-2problem-based lectures
M-1informative lectures
Sposób ocenyS-1Ocena podsumowująca: Based on written and oral assessment
S-3Ocena formująca: Didactic discussion
Kryteria ocenyOcenaKryterium oceny
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of the use of statistical methods
3,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 50-60% of the total score for final questions
3,5Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 61-70% of the total score for final questions
4,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 71-80% of the total score for final questions
4,5Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 81-90% of the total score for final questions
5,0Has knowledge in the field of statistical methods, documented by obtaining a score in the range of 91-100% of the total score for final questions
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięTI_1A_C32.2_W02Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction by means of neural networks
Odniesienie do efektów kształcenia dla kierunku studiówTI_1A_W04Ma szczegółową wiedzę związaną z wybranymi zagadnieniami w obszarze teleinformatyki.
Cel przedmiotuC-3Developing the student's ability to use basic adaptive rules in the problem of pattern classification
C-1To familiarize the student with methods of pattern recognition, cluster analysis and dimensionality reduction
C-2Introducing the student to the possibility of using learning systems under supervision and without supervision
Treści programoweT-W-7Neural networks
T-W-9Boosting classifiers
T-W-8Decomposition of multi-class problems
Metody nauczaniaM-1informative lectures
M-2problem-based lectures
M-3lectures with the use of a computer
Sposób ocenyS-3Ocena formująca: Didactic discussion
S-1Ocena podsumowująca: Based on written and oral assessment
Kryteria ocenyOcenaKryterium oceny
2,0Does not meet the requirements for obtaining a satisfactory grade by obtaining less than 50% of the total score on the exam questions in the field of neural networks
3,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 50-60% of the total score for final questions
3,5Has knowledge in the field of neural networks, documented by obtaining a score in the range of 61-70% of the total score for final questions
4,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 71-80% of the total score for final questions
4,5Has knowledge in the field of neural networks, documented by obtaining a score in the range of 81-90% of the total score for final questions
5,0Has knowledge in the field of neural networks, documented by obtaining a score in the range of 91-100% of the total score for final questions
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięTI_1A_C32.2_U01Student is able to use adaptive rules in pattern classification using statistical techniques
Odniesienie do efektów kształcenia dla kierunku studiówTI_1A_U09Potrafi dobrać właściwe metody i narzędzia do rozwiązywania różnych zadań w warunkach nie w pełni przewidywalnych.
Cel przedmiotuC-3Developing the student's ability to use basic adaptive rules in the problem of pattern classification
Treści programoweT-P-1Application of a selected method of a supervised statistical learning system in the problem of pattern recognition
Metody nauczaniaM-4project method
Sposób ocenyS-4Ocena formująca: Based on observations of group work
S-2Ocena podsumowująca: Based on the presentation of work results and as-built documentation
Kryteria ocenyOcenaKryterium oceny
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of the use of statistical methods
3,0Is able to use statistical methods for pattern classification, obtaining a score in the range of 50-60% in the assessment of a design task in this area
3,5Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 61-70% in the assessment of a design task in this area
4,0Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 71-80% in the assessment of a design task in this area
4,5Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 81-90% in the assessment of a design task in this area
5,0Is able to use statistical methods for the classification of patterns, obtaining a score in the range of 91-100% in the assessment of a design task in this area
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięTI_1A_C32.2_U02Student is able to use adaptive rules in pattern classification using neural networks
Odniesienie do efektów kształcenia dla kierunku studiówTI_1A_U09Potrafi dobrać właściwe metody i narzędzia do rozwiązywania różnych zadań w warunkach nie w pełni przewidywalnych.
Cel przedmiotuC-3Developing the student's ability to use basic adaptive rules in the problem of pattern classification
Treści programoweT-P-2Implementation of the selected neural network training method in a programming environment
Metody nauczaniaM-4project method
Sposób ocenyS-4Ocena formująca: Based on observations of group work
S-2Ocena podsumowująca: Based on the presentation of work results and as-built documentation
Kryteria ocenyOcenaKryterium oceny
2,0Does not meet the requirements for obtaining a satisfactory grade, obtaining a score below 50% in the assessment of a design task in the field of using a neural network for pattern classification
3,0Can use a neural network for pattern classification, obtaining a score in the range of 50-60% in the assessment of a design task in this area
3,5Can use a neural network for pattern classification, obtaining a score in the range of 61-70% in the assessment of a design task in this area
4,0Can use a neural network for pattern classification, obtaining a score in the range of 71-80% in the assessment of a design task in this area
4,5Can use a neural network for pattern classification, obtaining a score in the range of 81-90% in the assessment of a design task in this area
5,0Can use a neural network for pattern classification, obtaining a score in the range of 91-100% in the assessment of a design task in this area