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Teaching GuideTerm Faculty of Computer Science |
| Grao en Intelixencia Artificial |
 Subjects |
| Fundamentals of Machine Learning |
| Contents |
| Identifying Data | 2023/24 | |||||||||||||
| Subject | Fundamentals of Machine Learning | Code | 614G03018 | |||||||||||
| 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 |
| 1. Introduction | 1.1. Introduction to Machine Learning 1.2. Learning Paradigms 1.3. Inductive Learning 1.4. No free Lunch Theorems |
| 2. Supervised learning | 2.1. Introduction 2.2. Artificial Neural Networks 2.3. Support Vector Machines 2.4. Decision trees 2.5. Regression trees and regression model trees 2.6. Instance-based learning |
| 4. Methodologies in data analysis | 4.1. Training, evaluation and model selection methodologies 4.2. Methodologies of a data analysis project |
| 3. Evolutionary Computation | 3.1. Genetic Algorithms 3.2. Genetic Programming 3.3. Swarms and other Evolutionary Computation techniques |
| 5. Clustering | 5.1. Clustering methods |
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