Teaching GuideTerm Faculty of Computer Science |
Grao en Ciencia e Enxeñaría de Datos |
Subjects |
Machine Learning I |
Learning aims |
Identifying Data | 2020/21 | |||||||||||||
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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Learning outcomes | Study programme competences / results | ||
Understand the relationship between the complexity of learning models, training data features and overfitting, and know the mechanisms to avoid it. | A24 A25 |
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Develop skills to design the stages of a complete data analysis process based on automatic learning techniques. | B2 B7 B9 B10 |
C1 |
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Know how to correctly apply automatic learning techniques to obtain reliable and significant results. | A24 |
B3 B8 |
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Know the most representative and current techniques of unsupervised, semi-supervised and supervised learning, with and without reinforcement. | A24 |
B8 |
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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 |
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Identify appropriate data analysis techniques according to the problem. | A25 |
B3 B8 |
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Manage the most current tools and work environments in the field of machine learning. | A26 |
B2 B10 |
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