Checking date: 29/06/2020

Course: 2020/2021

Data Modelling
Study: Master in Information Health Engineering (359)

Coordinating teacher: ARTES RODRIGUEZ, ANTONIO

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

Type: Electives
ECTS Credits: 6.0 ECTS


Learning results and competences and skills that will be acquired.
Basic competences CB6 Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB7 Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. CB8 Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. CB9 Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. CB10 Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General competences CG1 Ability to maintain continuous education after his/her graduation, enabling him/her to cope with new technologies. CG2 Ability to apply the knowledge of skills and research methods related to engineering. CG3 Ability to apply the knowledge of research skills and methods related to Life Sciences. CG4 Ability to contribute to the widening of the frontiers of knowledge through an original research, part of which merits publication referenced at an international level. Specific competences CE4 Ability to use techniques for processing massive amounts of medical data and images. CE5 Ability to implement medical imaging and data processing methods.
Description of contents: programme
Data Modelling 1. Introduction to probability, linear algebra, and optimization. 2. Models for discrete and continuous data. Exponential families. 3. Markovian and state-space models. 4. Graphical models. Exact and approximate inference in graphical models. 5. Deep generative models
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number % Student Presence AF3 134 134 100% AF4 42 42 100% AF5 24 0 0% AF6 120 0 0% AF7 248 0 0% AF8 16 16 100% SUBJECT TOTAL 600 184 30,66%
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Andrew Gelman et al.. Bayesian Data Analysis. CRC Press. 2013
  • Christopher M Bishop. Patter Recognition and Machine Learning. Springer. 2006
  • David JC Mackay. Information Theroy, Inference and Learning Algorithms. Cambridge University Press. 2003
  • Kevin P Murphy. Machine Learning. A Probabilistic Perspective. MIT Press. 2012
  • Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press. 2019

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