|
Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Machine Learning I |
| Learning aims |
| 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 | ||||||||||
|
||||||||||||||
| Learning outcomes | Study programme competences | ||
| Understand the relationship between the complexity of learning models, training data features and overfitting, and know the mechanisms to avoid it. | A24 A25 |
||
| Develop skills to design the stages of a complete data analysis process based on automatic learning techniques. | B2 B7 B9 B10 |
C1 |
|
| Know how to correctly apply automatic learning techniques to obtain reliable and significant results. | A24 |
B3 B8 |
|
| Know the most representative and current techniques of unsupervised, semi-supervised and supervised learning, with and without reinforcement. | A24 |
B8 |
|
| Know the most representative learning techniques for the classic problems of classification, regression and clustering, and other less classic ones such as sorting problems, one class problems or multitasking. | A24 |
B8 |
|
| Identify appropriate data analysis techniques according to the problem. | A25 |
B3 B8 |
|
| Manage the most current tools and work environments in the field of machine learning. | A26 |
B2 B10 |
|
|
