This is a first term course, so no other courses of the Master programme are key for this course. However, it is highly desirable that students are familiarized with basic concepts from statistics.

After this course students will understand the principles underlying the general regression, classification and data analysis problems, and will become familiarized with the different approaches for dealing with them. Students will learn that, for the correct understanding of these problems, it is necessary to master three basic probability theory elements: 1) the likelihood, 2) the difference between a priori and a posteriori uncertainty, and 3) Bayes' Theorem. From a practical point of view, students will be presented different approaches for learning from data to solve these problems: non-parametric techniques, methods based on empirical risk minimization, or those that follow Bayesian principles. More specifically, the following list summarizes the main objectives of this course, enumerated as competences to be acquired by the students: - knowledge of the theoretic principles underlying several of the most important techniques for learning from data. - ability to apply such techniques on real problems, and to extract results and conclusions. - understanding of classic methods for estimation and classifications, and skills for their correct application. - ability to use machine learning tools: gaussian processes, support vector machines, non-parametric methods - knowledge of other data analysis problems, in particular, in the domain of natural language processing

Unit 0: Introduction to data processing Unit 1: Data preprocessing 1.1. Data normalization 1.2. Dimensionality reduction 1.3. Clustering Unit 2: Regression 2.1. The regression problem 2.2. Non-parametric regression: k-NN 2.3. Linear and polynomial least squares regression 2.4. Bayesian regression 2.5. Other regression algorithms Unit 3: Classification 3.1. Classification problema 3.2. Non-parametric methods: k-NN 3.3. Logistic regression 3.4. Other classification algorithms Tema 4: Deep learning 4.1. Introduction to pytorch 4.2. Multilayer perceptron 4.3. Convolutional Neural Networks Tema 5: Natural Language Processing 5.1. Text Preprocessing 5.2. Word embeddings 5.3. Transformers 5.4. Neural Topic Models

LECTURES AND PRACTICAL SESSIONS Theory sessions consist of lectures in which the basic concepts of the course will be introduced, illustrating them with a large number of examples. Exercises and problems similar to those to be proposed in the exam will also be solved along the course. LAB SESSIONS Sessions in which students will apply the concepts presented in the course with the help of a computer. Students will deal with real data analysis problems, and will have to evaluate the performance of the implemented systems