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Teaching GuideTerm Faculty of Computer Science |
| Grao en Enxeñaría Informática |
 Subjects |
| Machine Learning |
| Contents |
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| Identifying Data | 2023/24 | |||||||||||||
| Subject | Machine Learning | Code | 614G01038 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Third | Optional | 6 | ||||||||||
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| Topic | Sub-topic |
| Unit 1: Introducción | 1.1. Introduction to Machine Learning 1.2. Learning Paradigms 1.3. Inductive Learning 1.4. No free Lunch Theorems |
| Unit 2: Supervised Learning | 2.1. Introduction 2.2. Support-Vector Machines 2.3. Decision Trees and Rules 2.4. Regression. Regression Trees 2.5. Bayesian Learning 2.6. Instant-Based Learning 2.7. Artificial Neural Networks 2.8. Evaluation 2.9. Ensembles |
| Unit 3: Unsupervised Learning | 3.1. Unsupervised learning: clustering 3.2. Unsupervised neural networks |
| Unit 4: Reinforcement Learning | 4.1. Markov Decision Processes 4.2. Reinforcement Learning |
| Unit 5: Deep Learning | 5.1. Introduction 5.2. Convolutional Networks 5.3. Advanced models |
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