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Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Machine Learning III |
| Planning |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Machine Learning III | Code | 614G02026 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Third | Obligatory | 6 | ||||||||||
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| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A24 A25 B2 B3 B4 B8 C1 C4 | 21 | 21 | 42 |
| Collaborative learning | A24 A25 B2 B3 B4 B7 B8 B10 C1 C4 | 0 | 21 | 21 |
| Supervised projects | A24 A25 A26 B2 B3 B4 B7 B9 B10 C1 | 3 | 24 | 27 |
| ICT practicals | A24 A25 A26 B2 B3 B4 B7 B9 B10 C1 | 18 | 18 | 36 |
| Objective test | A24 A25 B2 B3 B4 B7 B8 B9 C1 C4 | 2 | 20 | 22 |
| 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. | ||||
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