Linear algebra Probability and Data Analysis Introduction to Statistical Modeling

Become familiar with different analytical tools, based on data, to make decisions Acquire skills in unsupervised learning to build clusters and decrease dimensionality in big datasets Develop skills for the main statistical and machine-learning tools in supervised learning: classification and regression Use these models to make practical predictions/classifications and perform analytical inferences Handle the R language for these tools

K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. K4: Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing. K5: Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S3: Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata. S4: Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques, and applying knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization S6: Ability to correctly identify classification problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of multivariate analysis as the basis for classification, clustering and dimension reduction methods S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

1. Introducción al aprendizaje estadístico 2. Evaluación de métodos de aprendizaje 3. Aprendizaje no supervisado 3a. Clustering 3b. Reducción de dimensión 4. Statistical classification 5. Casos de estudio

Theory (3 ECTS), Practice (3 ECTS). 50% lectures with teaching materials available on the Web. The other 50% practical sessions (computer labs).