Wydział Elektryczny - Automatyka i robotyka (S1)
Sylabus przedmiotu Artificial intelligence in automation and robotics:
Informacje podstawowe
| Kierunek studiów | Automatyka i robotyka | ||
|---|---|---|---|
| Forma studiów | studia stacjonarne | Poziom | pierwszego stopnia |
| Tytuł zawodowy absolwenta | inżynier | ||
| Obszary studiów | charakterystyki PRK, kompetencje inżynierskie PRK | ||
| Profil | ogólnoakademicki | ||
| Moduł | — | ||
| Przedmiot | Artificial intelligence in automation and robotics | ||
| Specjalność | przedmiot wspólny | ||
| Jednostka prowadząca | Katedra Automatyki i Robotyki | ||
| Nauczyciel odpowiedzialny | Krzysztof Jaroszewski <Krzysztof.Jaroszewski@zut.edu.pl> | ||
| Inni nauczyciele | |||
| ECTS (planowane) | 3,0 | ECTS (formy) | 3,0 |
| Forma zaliczenia | egzamin | Język | angielski |
| Blok obieralny | 13 | Grupa obieralna | 1 |
Formy dydaktyczne
Wymagania wstępne
| KOD | Wymaganie wstępne |
|---|---|
| W-1 | Knowledge of mathematics, in particular matrix, differential and integral calculus, and the basics of logic mathematics |
Cele przedmiotu
| KOD | Cel modułu/przedmiotu |
|---|---|
| C-1 | To familiarize the student with basic knowledge of methods used in evolutionary techniques, machine learning and fuzzy logic |
| C-2 | Developing the student's ability to use basic tools and select artificial intelligence methods solving problems in the area of automation and robotics. |
| C-3 | Stimulating the student's need for continuous education and improvement of professional and personal competences social. |
Treści programowe z podziałem na formy zajęć
| KOD | Treść programowa | Godziny |
|---|---|---|
| projekty | ||
| T-P-1 | Organizational activities. Presentation of problems to be solved. | 2 |
| T-P-2 | GA. Solving the task using classical methods. Formulation of the objective function and its implementation for GA. | 3 |
| T-P-3 | GA. Implementation of functions for: generation of the initial population and functions converting between decimal and binary systems. | 3 |
| T-P-4 | GA. Implementation of the function of selecting individuals. | 3 |
| T-P-5 | GA. Implementation of crossover, mutation and inversion operators. | 3 |
| T-P-6 | GA. Implementation of the main loop of the algorithm. | 3 |
| T-P-7 | GA. Development of the Graphical User Interface (GUI). | 3 |
| T-P-8 | ANN. Classifier design. | 3 |
| T-P-9 | ANN. Model training. | 3 |
| T-P-10 | ANN. Interpretation of results; comparison of the quality of operation of structures. | 3 |
| T-P-11 | Fuzzy logic – control system design. | 3 |
| T-P-12 | Presentation of the obtained results. Passing the form of classes. | 3 |
| 35 | ||
| wykłady | ||
| T-W-1 | Artificial intelligence - introduction. Applications of artificial intelligence. | 1 |
| T-W-2 | Evolutionary techniques: Classical Genetic Algorithm, evolutionary strategies. Objective function. | 1 |
| T-W-3 | Selection, crossover, mutation and inversion operators. Algorithm stopping condition. | 1 |
| T-W-4 | Genetic algorithm – application to solve problems in the field of automation. | 1 |
| T-W-5 | Artificial neuron model. Perceptron – classification. | 1 |
| T-W-6 | Multilayer networks. Training a neural network. Backpropagation algorithm. | 1 |
| T-W-7 | Recurrent networks. Self-organizing networks. Convolutional networks. | 1 |
| T-W-8 | Machine learning – application to solve problems in the field of automation. | 1 |
| T-W-9 | Expert system. Fuzzy inference system. | 1 |
| T-W-10 | Fuzzy logic – application to solve problems in the field of automation. | 1 |
| 10 | ||
Obciążenie pracą studenta - formy aktywności
| KOD | Forma aktywności | Godziny |
|---|---|---|
| projekty | ||
| A-P-1 | Participating in classes | 35 |
| A-P-2 | Execution of reports | 13 |
| A-P-3 | Consultancy | 2 |
| 50 | ||
| wykłady | ||
| A-W-1 | Participation in classes | 10 |
| A-W-2 | The study of literature | 8 |
| A-W-3 | Preparation for the exam | 5 |
| A-W-4 | The exam | 2 |
| 25 | ||
Metody nauczania / narzędzia dydaktyczne
| KOD | Metoda nauczania / narzędzie dydaktyczne |
|---|---|
| M-1 | Informative lecture |
| M-2 | Problem-oriented lecture |
| M-3 | Project exercises |
| M-4 | Computer-based lecture |
| M-5 | The project method |
| M-6 | Encouragement to deepen knowledge and expand skills |
Sposoby oceny
| KOD | Sposób oceny |
|---|---|
| S-1 | Ocena formująca: Based on observations of group work |
| S-2 | Ocena podsumowująca: Based on reports |
| S-3 | Ocena podsumowująca: Based on the presentation of work results and as-built documentation |
| S-4 | Ocena podsumowująca: Based on written and oral examination |
| S-5 | Ocena formująca: Didactic talk |
| S-6 | Ocena formująca: Monitoring the team's progress and commitment to 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 |
|---|---|---|---|---|---|---|---|
| AR_1A_C20.2_W01 The student has knowledge of basic methods artificial intelligence. Explains the idea of action and areas application of selected methods related to techniques evolutionary, machine learning and fuzzy logic. | AR_1A_W05 | — | — | C-1 | T-W-6, T-W-1, T-W-9, T-W-4, T-W-5, T-W-10, T-W-7, T-W-2, T-W-8, T-W-3 | M-1, M-4, M-2 | S-4, S-5 |
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 |
|---|---|---|---|---|---|---|---|
| AR_1A_C20.2_U01 The student is able to select and apply appropriate methods evolutionary techniques, machine learning and fuzzy logic solving a problem in the field of automation and robotics. | AR_1A_U04 | — | — | C-2 | T-P-5, T-P-3, T-P-12, T-P-11, T-P-6, T-P-10, T-P-4, T-P-2, T-P-8, T-P-9, T-P-1, T-P-7 | M-5, M-3 | S-2, S-3, S-1, S-6 |
Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne
| 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 |
|---|---|---|---|---|---|---|---|
| AR_1A_C20.2_K01 The student knows how to improve his/her competences. | AR_1A_K01 | — | — | C-3 | T-P-12 | M-6 | S-2, S-6, S-4, S-3 |
Kryterium oceny - wiedza
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| AR_1A_C20.2_W01 The student has knowledge of basic methods artificial intelligence. Explains the idea of action and areas application of selected methods related to techniques evolutionary, machine learning and fuzzy logic. | 2,0 | The student does not have knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. Obtained less than 50% of the total number of points on the assessment forms for this effect. |
| 3,0 | The student has knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. He obtained 50-60% of the total points on the assessment forms for this effect. | |
| 3,5 | The student has knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. He obtained 61-70% of the total points on the assessment forms for this effect. | |
| 4,0 | The student has knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. He obtained 71-80% of the total points on the assessment forms for this effect. | |
| 4,5 | The student has knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. He obtained 81-90% of the total points on the assessment forms for this effect. | |
| 5,0 | The student has knowledge of basic artificial intelligence methods: evolutionary techniques, machine learning, expert systems, fuzzy logic. He obtained 91-100% of the total points on the assessment forms for this effect. |
Kryterium oceny - umiejętności
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| AR_1A_C20.2_U01 The student is able to select and apply appropriate methods evolutionary techniques, machine learning and fuzzy logic solving a problem in the field of automation and robotics. | 2,0 | The student is unable to apply artificial intelligence methods in the area of automation and robotics. Obtained less than 50% of the total number of points on the assessment forms for this effect. |
| 3,0 | The student is able to apply artificial intelligence methods in the field of automation and robotics. He obtained 50-60% of the total points on the assessment forms for this effect. | |
| 3,5 | The student is able to apply artificial intelligence methods in the field of automation and robotics. He obtained 61-70% of the total points on the assessment forms for this effect. | |
| 4,0 | The student is able to apply artificial intelligence methods in the field of automation and robotics. He obtained 71-80% of the total points on the assessment forms for this effect. | |
| 4,5 | The student is able to apply artificial intelligence methods in the field of automation and robotics. He obtained 81-90% of the total points on the assessment forms for this effect. | |
| 5,0 | The student is able to apply artificial intelligence methods in the field of automation and robotics. He obtained 91-100% of the total points on the assessment forms for this effect. |
Kryterium oceny - inne kompetencje społeczne i personalne
| Efekt uczenia się | Ocena | Kryterium oceny |
|---|---|---|
| AR_1A_C20.2_K01 The student knows how to improve his/her competences. | 2,0 | The student does not know and is not willing to learn how to improve his/her competences. |
| 3,0 | The student knows how to improve his/her competences. The student obtained 50-60% of the total number of points in the assessment forms for this effect. | |
| 3,5 | The student knows how to improve his/her competences. The student obtained 61-70% of the total number of points in the assessment forms for this effect. | |
| 4,0 | The student knows how to improve his/her competences. The student obtained 71-80% of the total number of points in the assessment forms for this effect. | |
| 4,5 | The student knows how to improve his/her competences. The student obtained 81-90% of the total number of points in the assessment forms for this effect. | |
| 5,0 | The student knows how to improve his/her competences. The student obtained 91-100% of the total number of points in the assessment forms for this effect. |
Literatura podstawowa
- Negnevitsky Michael, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, Wssex, 2005, 2nd edition