Teaching GuideTerm Faculty of Computer Science |
Grao en Ciencia e Enxeñaría de Datos |
Subjects |
Machine Learning I |
Contents |
Identifying Data | 2024/25 | |||||||||||||
Subject | Machine Learning I | Code | 614G02019 | |||||||||||
Study programme |
|
|||||||||||||
Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
|
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 |
|