13D111VIE - Artificial Intelligence and Expert Systems
| Course specification | ||||
|---|---|---|---|---|
| Course title | Artificial Intelligence and Expert Systems | |||
| Acronym | 13D111VIE | |||
| Study programme | Electrical Engineering and Computing | |||
| Module | Software Engineering | |||
| Type of study | doctoral studies | |||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 9.0 | Status | elective | |
| Condition | none | |||
| The goal | Introduce students to the methodologies and applications of modern development in artificial intelligence area, with an emphasis on advanced technologies. | |||
| The outcome | The learning outcomes of the course are to enable students to implement various concepts and technologies in artificial intelligence applications. They are able to identify characteristics of the system and select the best and most effective solutions. | |||
| Contents | ||||
| Contents of lectures | Knowledge and reasoning - BPN, Bayes's rule, semantics and reasoning in Bayes's network; decision support - the basis of the theory model; working with multiple agents, game theory. Learning - forms of learning, decision trees, machine learning theory. Genetic algorithms. Search and semantics . Effective interpretation, search and processing of a data set. | |||
| Contents of exercises | ||||
| Literature | ||||
| ||||
| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 6 | ||||
| Methods of teaching | Presentation, individual work, discussion | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | 0 | Test paper | 0 | |
| Practical lessons | 0 | Oral examination | 30 | |
| Projects | ||||
| Colloquia | 0 | |||
| Seminars | 70 | |||