Data analysis and visualization Probability Statistical Inference I Statistical Inference II Multivariate Analysis Regression methods Advance regression methods Stochastic processes Statistical learning Time series

AF1: THEORETICAL-PRACTICAL CLASSES. They will present the knowledge that students should acquire. They will receive the class notes and will have basic texts of reference to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems on the part of the student will be solved and workshops and evaluation test will be held to acquire the necessary skills. AF2: Updated to allegation AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAMINATION In which the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: CLASS THEORY. Exhibitions in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical cases, problems, etc. raised by the teacher individually or in groups. MD3: TUTORIES. Individualized assistance (individual tutorials) or group (collective tutorials) to students by the teacher.

1. Introduction to functional data analysis a. Some examples of functional data. b. Mathematical tools for functional data. c. Functional data smoothing. d. Functional random variables. e. Functional mean, variance and covariance. f. The covariance operator. g. Main functional components. 2. Inference for functional data: a. Inference in the functional context. b. Functional mean, variance and covariance of the sample. c. The sample covariance operator. d. Sample functional principal components. e. Depth measurements for functional data. f. Detection of outliers in functional data. 3. Regression for functional data: a. The functional regression problem. b. Functional regression with scalar response. c. Functional regression with functional response. 4. Supervised classification for functional data: a. The functional supervised classification problem. b. Base expansion classifiers. c. Classifiers on functional principal component scores. d. Functional logistic regression. e. Functional generalized additive models. f. Non-parametric functional classification. g. Functional nearest neighbors. f. Depth-based classifiers. 5. Unsupervised classification for functional data: a. The functional unsupervised classification problem. b. Methods based on base expansions. c. Methods based on functional principal component scores. d. Partition methods for functional data. e. Hierarchical methods for functional data. f. Model-based methods for functional data.