Checking date: 09/05/2019


Course: 2019/2020

Machine Learning
(18054)
Study: Master in Information Health Engineering (359)
EPI


Coordinating teacher: GOMEZ VERDEJO, VANESSA

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

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Competences and skills that will be acquired and learning results.
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.
Description of contents: programme
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 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 100 100 100% AF4 32 32 100% AF5 18 0 0% AF6 90 0 0% AF7 186 0 0% AF8 12 12 100% TOTAL SUBJECT 450 138 30,6%
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • C. E. Rasmussen. Gaussian Processes for Machine Learning. MIT Press. 2006
  • C. M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006
  • R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification (2nd ed.). Wiley Interscience. 2001
  • T. Hastie, R. Tibshirani, J. Friedman . The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition . Springer. 2009

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