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