Checking date: 24/11/2023

Course: 2023/2024

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
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% 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.