13E083MOAR - Mathematical Basis of Automatic Reasoning
| Course specification | ||||
|---|---|---|---|---|
| Course title | Mathematical Basis of Automatic Reasoning | |||
| Acronym | 13E083MOAR | |||
| 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 | 3.0 | Status | elective | |
| Condition | ||||
| The goal | Familiarize students with basic concepts of logical modeling and reasoning that are used in automated control and management of systems. Introducing students to Bayesian and causal networks, as well as Bayesian estimation. | |||
| The outcome | Students get a mathematical basis and also practical hints in treating problems within the scope of their discipline. | |||
| Contents | ||||
| Contents of lectures | Formalisations, modelling of knowledge and problems using classical nad nonclassical logic (fuzzy logic, linear logic, modal and temporal logic). Uncertain knowledge and reasoning (kvantification, probability reasoning). Automated reasoning, perception, planning and acting. Bayseian and causal networks. Estimation of parametars and Bayesian estimation. | |||
| Contents of exercises | Through examples, tasks and problems student learns how to apply theorems and basic concepts that are learnt through theoretical contents. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 1 | 1 | 0.5 | ||
| Methods of teaching | Lectures, tutorials and discussions. | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | Test paper | 30 | ||
| Practical lessons | Oral examination | |||
| Projects | 70 | |||
| Colloquia | ||||
| Seminars | ||||