Basic knowledge of statistical software R or similar.

The objective of this course is to train students in the use of advanced data analysis techniques applied to the connected industry. Data visualization tools will be used, and advanced machine learning models will be implemented. By the end of the course, students will be equipped to analyze data, identify patterns, and apply predictive solutions in industrial environments, contributing to process optimization and data-driven decision-making.

1. Introduction 1.1 Basics of Multivariate Data Analysis 1.2 Introduction to Statistical Learning 1.3 Supervised vs. Unsupervised Learning 1.4 Data Visualization Techniques 2. Supervised Learning: Regression 2.1 Linear Regression 2.2 Linear Model Selection and Regularization 2.3 Cross-Validation on Regression problems 2.4 Extensions 3. Supervised Learning: Classification 3.1 Logistic Regression 3.2 Bayes classifier 3.3 Linear Discriminant Analysis 3.4 k-Nearest Neighbor classifier 3.5 Random Forests 3.6 Support Vector Machines 3.7 Cross-Validation on Classification problems 4. Unsupervised Learning and Dimensionality Reduction Techniques 4.1 Clustering methods: k-means and hierarchical clustering 4.2 Principal Component Analysis 4.3 Multidimensional Scaling 4.4 ISOMAP and Locally-Linear Embedding

LEARNING ACTIVITIES: AF3: Theoretical-Practical classes. AF6: Group work. AF7: Individual student work. AF8: Partial and final exams. METHODOLOGY: MD1: Theoretical lessons, with support material available on the Web, to present and develop the main concepts of the course. Teachers with provide students with supplementary material. MD2: Critical reading of documents provided by the teachers: newspaper articles, reports, manuals and / or academic papers, either for later discussion in class, either to expand and consolidate the knowledge of the subject. MD3: Resolution of practical cases, problems, etc. proposed by the teacher individually or in groups. MD4: Preparation of projects individually or in group. TUTORING SESSIONS: - Weekly individual tutoring sessions - Group tutorials might be possible