Checking date: 16/04/2024

Course: 2024/2025

Machine Learning
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: GOMEZ VERDEJO, VANESSA

Department assigned to the subject: Signal and Communications Theory Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Linear algebra. Multivariable calculus. Statistics. Introduction to Machine Learning (or similar)
The goal of this course is that the student knows the different advanced machine learning techniques to solve classification, regression and unsupervised problems so that he/she is then able to implement, train and validate different algorithms depending on the available data or, even, propose and formulate modified or new models depending on the needs of the problem.
Skills and learning outcomes
Description of contents: programme
Machine Learning * Review of data analysis and preprocessing. * Ensembles for classification/regression * Kernel methods: Support Vector Machines for classification and regression * Gaussian processes * Unsupervised learning: spectral clustering, novelty detection * Dimensionality reduction with kernel methods: KPCA, KPLS,.... * Feature selection
Learning activities and methodology
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.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
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
Detailed subject contents or complementary information about assessment system of B.T.

The course syllabus may change due academic events or other reasons.