|
Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| AI Fundamentals |
| Methodologies |
|
|
|
| Identifying Data | 2022/23 | |||||||||||||
| Subject | AI Fundamentals | Code | 614544001 | |||||||||||
| Study programme |
|
|||||||||||||
| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
First | Obligatory | 3 | ||||||||||
|
||||||||||||||
| Methodologies | Description |
| Laboratory practice | Laboratory practices: the teaching staff of the subject poses to the students a problem or problems of a practical nature whose resolution requires the understanding and application of the theoretical-practical contents included in the contents of the subject. Students can work on the solution to the problems raised individually or in groups. This teaching methodology will be applied to the training activity "Practical laboratory classes" and may be applied to the training activity of "Problem-based learning sessions, seminars, case studies and projects" |
| Problem solving | Learning by projects: students are presented with practical projects whose scope requires that an important part of the total dedication of the student be devoted to the subject. In addition, due to the scope of the work to be carried out, it is required not only that the students apply management skills as well as technical skills. |
| Guest lecture / keynote speech | Expository method / master class: the teacher presents a topic to the students with the aim of providing a set of information with a specific scope. This teaching methodology will be applied to the training activity "Theory classes" |
|
