OS3ESM - Expert Systems Decision Making
| Course specification | ||||
|---|---|---|---|---|
| Course title | Expert Systems Decision Making | |||
| Acronym | OS3ESM | |||
| Study programme | Electrical Engineering and Computing | |||
| Module | Signals and Systems | |||
| Type of study | bachelor academic studies | |||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 6.0 | Status | elective | |
| Condition | ||||
| The goal | Introducing students to the basic concepts and techniques of artificial intelligence and expert systems. During the course students will learn the most popular models for the implementation of these types of applications. Students will be trained to recognize a problem that belongs to the field of artificial intelligence and expert systems, and based on his knowledge application najpodesniju and most effective method for its solution. | |||
| The outcome | ||||
| Contents | ||||
| Contents of lectures | Search Strategy: possible algorithms, performance, efficiency, complexity. Concluding systems - semantic networks and descriptive logic; Trip - kind of problem. Knowledge and reasoning and uncertain environment, Fuzzy logic. Formation, representation and opreacije of fuzzy sets Evan conclusion. Problem - solving strategies. Strips algorithm. Induction systems. Visual simulation theoretically treated the problem. Solving practical tasks and demonstrations. | |||
| Contents of exercises | ||||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 3 | 1 | 1 | ||
| Methods of teaching | ||||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | 0 | Test paper | 40 | |
| Practical lessons | 0 | Oral examination | 0 | |
| Projects | 20 | |||
| Colloquia | 40 | |||
| Seminars | 0 | |||