Linear algebra. Multivariable calculus. Statistics.

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. 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. 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 CE1 Ability to know the peculiarities of data acquisition and information processing in the field of biomedical signals and images. CE2 Ability to design and implement automatic learning systems for supervised and unsupervised problem solving. CE3 Ability to design estimation and decision procedures from signals and images using statistical modeling.

Machine Learning Introduction to Machine learning. Linear methods: linear and logistic regression. Kernel methods: SVMs y GPs Clustering: K-means and spectral clustering Dimensionality reduction: PCA, PLS, feature selection

LEARNING ACTIVITIES The following training activities will be used for the development of the course AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams METHODOLOGY MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the course are developed and complemented with bibliography. MD2: Critical reading of texts recommended by the professor of the course. MD3: Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the course, as well as case studies. MD5: Elaboration of works and reports individually or in groups. Mainly, the classes will be developed with Python notebooks with practical theoretical content and will be complemented with practices to be carried out by the student individually or in groups. TUTORING REGIME There will be 2 hours a week of tutoring for students where the teacher will be available in his office.