Checking date: 04/06/2021

Course: 2021/2022

Machine Learning
Study: Master in Applied and Computational Mathematics (372)

Coordinating teacher: GOMEZ VERDEJO, VANESSA

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

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
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 Collect and interpret data of a mathematical nature which can be applied to other domains of scientific knowledge. CG2 Apply acquired knowledge and possess the ability to solve novel problems related with Mathematics. CG3 Being able to develop new scientific/technological approaches in a corporate environment. CG6 Being able to autonomously study and do research. Specific competences CE12 Being able to know the peculiarities of data acquisition and information management. CE13 Ability to design and implement automatic learning systems for supervised and unsupervised problem solving. CE14 Acquire an innovative attitude and approach.
Skills and learning outcomes
Description of contents: programme
1. Introduction to machine learning. 2. Linear methods: linear and logistic regression. 3. Kernel methods: SVMs and GPs. 4. Clustering: K-means and spectral clustering. 5. 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%
Assessment System
  • % end-of-term-examination 10
  • % of continuous assessment (assigments, laboratory, practicals...) 90
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

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