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Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| Machine Learning I |
| Learning aims |
| Identifying Data | 2022/23 | |||||||||||||
| Subject | Machine Learning I | Code | 614544012 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
First | Obligatory | 6 | ||||||||||
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| Learning outcomes | Study programme competences | ||
| Ability to identify if a problem can be solved using a machine learning technique. | AC12 |
BC2 BC3 BC4 BC8 |
CC4 CC7 CC8 CC9 |
| Obtain the ability to choose the most appropriate learning technique for a problem depending on the nature of the data. | AC11 AC15 |
BC2 BC6 BC7 BC9 |
CC3 CC8 |
| Ability to design and develop a learning model in a real programming environment. | AC10 AC15 |
BC5 BC6 BC7 BC8 BC9 |
CC3 CC7 CC9 |
| Master the different learning models and be able to apply them to real-world problems. | AC11 AC15 |
BC2 BC3 BC7 |
CC3 CC8 |
| Know and understand the difference between classification and regression problems. | AC10 AC11 |
BC3 BC6 BC8 |
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| Understand how to compare the results of the different types of machine learning. | AC10 AC12 AC15 |
BC7 BC9 |
CC4 CC8 CC9 |
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