Checking date: 24/04/2024


Course: 2024/2025

Statistical Signal Processing
(19274)
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)
EPI


Coordinating teacher: RAMIREZ GARCIA, DAVID

Department assigned to the subject: Signal and Communications Theory Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
STUDENTS ARE EXPECTED TO HAVE COMPLETED: Introduction to Statistical Signal Processing (or similar)
Objectives
The main objective of this course is that the student is able to extract relevant information contained in the signals under study with the aid of statistical signal processing tools. To achieve this, the student will study advanced techniques of: - Random signals analysis - Estimation - Detection
Skills and learning outcomes
Description of contents: programme
DESCRIPTION OF CONTENTS: PROGRAMME · Parameter estimation - Method of moments - Maximum likelihood - Bayesian estimation · Signal Estimation - MMSE estimation - Linear estimation and prediction - Optimal and adaptive filtering · Hypothesis testing - Wald tests - Likelihood ratio methods - Bayesian tests
Learning activities and methodology
LEARNING ACTIVITIES 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. CONSULTATION HOURS The students will be able to consult with the instructor during 2 or 3 hours per week
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment


Basic Bibliography
  • C. P. Robert, G. Casella. Monte Carlo Statistical Methods. Springer. 2004
  • H. Stark, J. W. Woods. Probability and Random Processes with Applications to Signal Processing. Prentice-Hall. 2002
  • L. Wasserman. All of statistics. Springer. 2013
  • V. Poor. An introduction to signal detection and estimation. Springer. 1994

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