|
Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Machine Learning I |
| Planning |
| Identifying Data | 2021/22 | |||||||||||||
| Subject | Machine Learning I | Code | 614G02019 | |||||||||||
| Study programme |
|
|||||||||||||
| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
|
||||||||||||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A24 A25 B3 B8 B9 | 30 | 60 | 90 |
| Laboratory practice | A26 B2 B3 B10 C1 | 18 | 18 | 36 |
| Supervised projects | B2 B3 B7 B9 B10 | 10 | 10 | 20 |
| Objective test | A24 A25 B8 B9 | 2 | 0 | 2 |
| Personalized attention | 2 | 0 | 2 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
|
