Introduction
This course explores fundamental concepts and algorithms in Machine Learning, focusing on both supervised and unsupervised learning. Students learn how to build, evaluate, and optimize ML models using practical data-driven approaches (60 hours).
- Emilio Serrano, 14h
- Esteban Garcia, 30h
- Bojan Mihaljevic, 8h
- Roberto Valle, 8h
Unit 1: Introduction
- Fundamentals of Machine Learning
- Data preprocessing
Unit 2: Unsupervised Learning
- Introduction to clustering
- Evaluation metrics for clustering
- Partition-based clustering
- Hierarchical clustering
Unit 3: Feature Selection
- Filter methods
- Wrapper methods
- Embedded methods
Unit 4: Supervised Learning
- Introduction to classification and regression
- Basic classification/regression methods
- Evaluation metrics for supervised models
- Advanced classification and regression methods