Checking date: 24/04/2024


Course: 2024/2025

Introduction to Statistical Signal Processing
(19604)
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: Additional training
ECTS Credits: 2.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
The student should have basic knowledge of - probability theory and statistics - linear algebra.
Objectives
The main objective of this course is that the student acquires the basic knowledge/tools to be able to complete Statistical Signal Processing
Skills and learning outcomes
Description of contents: programme
Probability theory: introduction, random variables, probability distribution and density functions, mathematical expectation and moments. Stochastic processes: introduction, first- and second-order statistics, stationarity and power spectral density Linear algebra: introduction, matrix algebra and matrix decompositions
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 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100

Calendar of Continuous assessment


Basic Bibliography
  • A. Papoulis and S. Pillai. Probability, Random Variables, and Stochastic Processes . McGraw-Hill. 2002
  • D. Ramírez, I. Santamaría, and L. Scharf. Coherence: In Signal Processing and Machine Learning. Springer. 2023

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