Teaching GuideTerm Faculty of Computer Science |
Grao en Ciencia e Enxeñaría de Datos |
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Identifying Data | 2022/23 | |||||||||||||
Subject | Machine Learning I | Code | 614G02019 | |||||||||||
Study programme |
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Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
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Methodologies / tests | Competencies / Results | Teaching hours (in-person & virtual) | 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. |
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