Checking date: 04/05/2020

Course: 2019/2020

Data Processing
Study: Master in Telecommunications Engineering (227)

Coordinating teacher: ARENAS GARCIA, JERONIMO

Department assigned to the subject: Department of Signal and Communications Theory

Type: Compulsory
ECTS Credits: 6.0 ECTS


Students are expected to have completed
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.
Competences and skills that will be acquired and learning results. Further information on this link
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 - knowledge of other data analysis problems, like topic modeling or recommendations systems
Description of contents: programme
Unit 0: Introduction to data processing Unit 1: Regression 1.1. The regression problem 1.2. Non-parametric regression: k-NN 1.3. Linear and polynomial least squares regression 1.4. Bayesian regression Unit 2: Classification 2.1. Classification problema 2.2. Non-parametric methods: k-NN 2.3. Logistic regression 2.4. Neural Networks Unit 3: Data clustering 3.1. k-means clustering 3.2. Spectral clustering Unit 4: Topic models 4.1. Text analysis 4.2. Latent Dirichlet Allocation
Learning activities and methodology
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
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Basic Bibliography
  • C. E. Rasmussen. Gaussian Processes for Machine Learning. MIT Press. 2006
  • R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification (2nd ed.). Wiley Interscience. 2001
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • C. M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus and the academic weekly planning may change due academic events or other reasons.