Checking date: 30/04/2019

Course: 2019/2020

Machine Learning
Study: Master in Advanced Communications Technologies (278)

Coordinating teacher: CID SUEIRO, JESUS

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

Type: Electives
ECTS Credits: 6.0 ECTS


Students are expected to have completed
This is a first term course, so no other courses of the Master programme are key for this course. However, it is highly desirable that students are familiarized with basic concepts from statistics.
Competences and skills that will be acquired and learning results.
After this course students will understand the principles of estimation, decision, and grouping problems, and will become familiarized with the different approaches for dealing with them. Students will understand that, for the correct understanding of these problems, it is necessary to master three basic probability theory elements: 1) the likelihood, 2) the difference between a priori and a posteriori uncertainty, and 3) Bayes' Theorem. From a practical point of view, students will be presented different approaches for learning from data to solve these problems: non-parametric techniques, methods based on empirical risk minimization, or those that follow Bayesian principles. More specifically, the followint list summarizes the main objectives of this course, enumerated as competences to be acquired by the students: - knowledge of the theoretic principles underlying several of the most important techniques for learning from data. - ability to apply such techniques on real problems, and to extract results and conclusions. - understanding of clasic methods for estimation and clasifications, and skills for their correct application. - ability to use machine learning tools: neural networks, support vector machines, etc. - knowledge of last generation approaches, including those based on machine ensembles
Description of contents: programme
Unit 0: Introduction to data processing Unit 1: Bayes' Estimation and Decision Theory 1.1. General overview of the estimation and decision problems 1.2. Bayes' Theorem 1.3. Bayes Estimation Theory. MSE, MAD, and MAP estimators 1.4. ML Estimation 1.5. Optimum Bayes' classifier for the binary and multiclass cases 1.6. Characterization of binary classifiers Unit 2: Regression 2.1. The regression problem 2.2. Non-parametric regression: k-NN 2.3. Linear and polynomial least squares regression 2.4. Bayesian regression 2.5. Gaussian processes Unit 3: Classification 3.1. Classification problema 3.2. Non-parametric methods: k-NN 3.3. Logistic regression 3.4. Support vector machines Unit 4: Data clustering 4.1. k-means clustering 4.2. Spectral clustering
Learning activities and methodology
LECTURES AND PRACTICAL SESSIONS Theory sessions consist of lectures in which the basic concepts of the course will be introduced, illustrating them with a large number of examples. Exercises and problems similar to those to be proposed in the exam will also be solved along the course. LAB SESSIONS Sessions in which students will apply the concepts presented in the course with the help of a computer. Students will deal with estimation and classification problems with real data, and will have to evaluate the performance of the implemented systems RESEARCH PROJECT AND DISCUSSIONS Students will be given a list of topics related to the research areas of the course, so that they can prepare a project on one of them. The work will be presented to the class on specific sessions.
Assessment System
  • % end-of-term-examination 25
  • % of continuous assessment (assigments, laboratory, practicals...) 75
Basic Bibliography
  • C. E. Rasmussen. Gaussian Processes for Machine Learning. MIT Press. 2006
  • R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification (2nd ed.). Wiley Interscience. 2001
Additional Bibliography
  • C. M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006

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