| Educational guide School of Engineering |
english |
| Computer Security Engineering and Artificial Intelligence (2016) |
 Subjects |
| ARTIFICIAL VISION AND PATTERN RECOGNITION |
| Assessment |
| IDENTIFYING DATA | 2023_24 |
| Subject | ARTIFICIAL VISION AND PATTERN RECOGNITION | Code | 17685105 | |||||
| Study programme |
|
Cycle | 2nd | |||||
| Descriptors | Credits | Type | Year | Period | ||||
| 4.5 | Compulsory | First | 1Q |
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| Competences | Learning outcomes | Contents |
| Planning | Methodologies | Personalized attention |
| Assessment | Sources of information | Recommendations |
| Methodologies | Competences | Description | Weight | ||||||||||
| IT-based practicals in computer rooms |
|
Elaboration by the students of practical work related to the main topics of the course using the tools of computer vision explained in the practical classes. Elaboration of a report. | 40 | ||||||||||
| Presentations / oral communications |
|
Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Oral presentation. Final evaluation by the teacher. | 30 | ||||||||||
| Short-answer objective tests |
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Objective short-answer tests | 30 | ||||||||||
| Others |
| Other comments and second exam session | |||
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In all tests (practical, oral, written) a minimum of 4 points (out of 10) must be obtained. The weighted average of all tests must be 5 points (out of 10) to pass the subject. Students who do not pass the continuous assessment can recover the parts suspended or not presented in the second call. In all written exams, you may not use any type of electronic device. |
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