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Teaching GuideTerm Faculty of Computer Science |
Guía Provisional
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| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Machine Learning I |
| Contents |
| Identifying Data | 2024/25 | |||||||||||||
| 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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| Topic | Sub-topic |
| Introduction | Introduction to Machine Learning Learning Paradigms Inductive Learning No Free Lunch Theorems |
| Supervised learning | Introduction Artificial Neural Networks Logistic Regression Support Vector Machines Decision Trees Instance-based learning ML Models for Regression |
| Evolutionary Computation | Genetic Algorithms Genetic Programming Swarms and other Evolutionary Computation techniques |
| Methodologies in data analysis | Training, evaluation and model selection methodologies Methodologies of a data analysis project |
| Unsupervised learning | Clustering methods Self-organised networks |
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