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
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | Knowledge of mathematics at an engineering level |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | To familiarize the student with methods of pattern recognition, cluster analysis and dimensionality reduction |
| C-2 | Introducing the student to the possibility of using learning systems under supervision and without supervision |
| C-3 | Developing the student's ability to use basic adaptive rules in the problem of pattern classification |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| projekty | ||
| T-P-1 | Application of a selected method of a supervised statistical learning system in the problem of pattern recognition | 7 |
| T-P-2 | Implementation of the selected neural network training method in a programming environment | 8 |
| 15 | ||
| wykłady | ||
| T-W-1 | Introduction to statistics | 2 |
| T-W-2 | Probabilistic classification methods | 2 |
| T-W-3 | Regression methods | 1 |
| T-W-4 | ROC curves | 1 |
| T-W-5 | Support vector machines | 1 |
| T-W-6 | Nearest neighbour method | 1 |
| T-W-7 | Neural networks | 3 |
| T-W-8 | Decomposition of multi-class problems | 1 |
| T-W-9 | Boosting classifiers | 1 |
| T-W-10 | Principal component analysis | 1 |
| T-W-11 | Clustering and correspondence analysis. Passing the lectures. | 1 |
| 15 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| projekty | ||
| A-P-1 | participation in classes | 15 |
| A-P-2 | individual work on the project | 8 |
| A-P-3 | Consultancy | 2 |
| 25 | ||
| wykłady | ||
| A-W-1 | participation in lectures | 15 |
| A-W-2 | literature studies | 5 |
| A-W-3 | preparation for course passing | 5 |
| 25 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | informative lectures |
| M-2 | problem-based lectures |
| M-3 | lectures with the use of a computer |
| M-4 | project method |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena podsumowująca: Based on written and oral assessment |
| S-2 | Ocena podsumowująca: Based on the presentation of work results and as-built documentation |
| S-3 | Ocena formująca: Didactic discussion |
| S-4 | Ocena formująca: Based on observations of group work |
Zamierzone efekty uczenia się - wiedza
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Odniesienie do efektów uczenia się prowadzących do uzyskania tytułu zawodowego inżyniera | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|---|
| TI_1A_C32.2_W01 Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction with multivariate statistical methods | TI_1A_W04 | — | — | C-2, C-1, C-3 | T-W-10, T-W-1, T-W-5, T-W-3, T-W-4, T-W-2, T-W-11, T-W-6 | M-3, M-2, M-1 | S-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_W04 | — | — | C-3, C-1, C-2 | T-W-7, T-W-9, T-W-8 | M-1, M-2, M-3 | S-3, S-1 |
Zamierzone efekty uczenia się - umiejętności
| Zamierzone efekty uczenia się | Odniesienie do efektów kształcenia dla kierunku studiów | Odniesienie do efektów zdefiniowanych dla obszaru kształcenia | Odniesienie do efektów uczenia się prowadzących do uzyskania tytułu zawodowego inżyniera | Cel przedmiotu | Treści programowe | Metody nauczania | Sposób oceny |
|---|---|---|---|---|---|---|---|
| TI_1A_C32.2_U01 Student is able to use adaptive rules in pattern classification using statistical techniques | TI_1A_U09 | — | — | C-3 | T-P-1 | M-4 | S-4, S-2 |
| TI_1A_C32.2_U02 Student is able to use adaptive rules in pattern classification using neural networks | TI_1A_U09 | — | — | C-3 | T-P-2 | M-4 | S-4, S-2 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| TI_1A_C32.2_W01 Acquiring knowledge concerning pattern recognition, cluster analysis and dimensionality reduction with multivariate statistical methods | 2,0 | Does 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 |
| 3,0 | Has 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 | |
| 3,5 | Has 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 | |
| 4,0 | Has 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 | |
| 4,5 | Has 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 | |
| 5,0 | Has 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 | 2,0 | Does 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 |
| 3,0 | Has 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 | |
| 3,5 | Has 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 | |
| 4,0 | Has 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 | |
| 4,5 | Has 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 | |
| 5,0 | Has 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 się | Ocena | Kryterium oceny |
|---|---|---|
| TI_1A_C32.2_U01 Student is able to use adaptive rules in pattern classification using statistical techniques | 2,0 | Does 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 |
| 3,0 | Is 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 | |
| 3,5 | Is 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 | |
| 4,0 | Is 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 | |
| 4,5 | Is 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 | |
| 5,0 | Is 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 | 2,0 | Does 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 |
| 3,0 | Can 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 | |
| 3,5 | Can 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 | |
| 4,0 | Can 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 | |
| 4,5 | Can 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 | |
| 5,0 | Can 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
- Russel S.J., Norvig P., Artificial Intelligence. A Modern Approach, Pearson, Hoboken, USA, 2024, Fourth
- Duda R. O., Hart P. E. and Stork D. G., Pattern Classification, John Wiley & Sons, New York, 2001, Second
- Luger G. F., Principles of Artificial Intelligence, Springer Nature, Heidelberg, 2024
Literatura dodatkowa
- Krzyśko M., Wołyński W., Górecki T., Skorzybut M., Systemy Uczące Się, Wydawnictwo Naukowo-Techniczne, Warszawa, 2008
- Osowski S., Metody Sztucznej Inteligencji, Oficyna Wydawnicz PW, Warszawa, 2000
- Rutkowski L., Metody i Techniki Sztucznej Inteligencji, PWN, Warszawa, 2023, Drugie