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Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| AI in Big Data Environments |
| Learning aims |
| Identifying Data | 2023/24 | |||||||||||||
| Subject | AI in Big Data Environments | Code | 614544016 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
Second | Optional | 6 | ||||||||||
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| Learning outcomes | Study programme competences | ||
| Know the techniques that allow the design of scalable AI techniques at software and hardware resources level. | AC10 AC11 AC12 AC15 |
BC2 BC7 |
CC3 CC4 |
| Acquire the skills to integrate large volume and variety of data in AI Big Data projects. | AC10 AC11 AC12 AC15 |
BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| To know the scalability paradigms in machine learning algorithms. | AC10 AC11 AC12 AC15 |
BC2 BC3 BC4 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Understand, analyze and design the necessary infrastructures for Big Data AI projects: local/cloud environment and physical/virtual equipment with low latency storage systems and distributed file systems | AC12 AC15 |
BC2 BC6 BC7 BC8 |
CC3 CC4 CC7 CC9 |
| To know the languages, frameworks and components that allow us to increase performance in hardware infrastructures with CPU and GPU. | AC11 AC15 |
BC3 BC7 BC8 |
CC3 CC4 CC7 CC9 |
| To know the techniques that allow, with low latency, the visualization of data in environments with large volume of information. | AC11 AC12 AC15 |
BC2 BC3 BC5 BC6 BC7 BC8 BC9 |
CC3 CC4 CC7 CC8 CC9 |
| Use and be able to apply the correct KPIs in each environment. | AC10 AC11 AC15 |
BC2 BC3 BC7 BC8 |
CC3 CC9 |
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