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Code: | M4110 | Acronym: | M4110 | Level: | 400 |

Keywords | |
---|---|

Classification | Keyword |

OFICIAL | Mathematics |

Active? | Yes |

Web Page: | https://moodle.up.pt/course/view.php?id=3583 |

Responsible unit: | Department of Mathematics |

Course/CS Responsible: | Master's degree in Data Science |

Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|

M:DS | 3 | Official Study Plan since 2018_M:DS | 1 | - | 6 | 42 | 162 |

Teacher | Responsibility |
---|---|

Ana Paula de Frias Viegas Proença Rocha |

Theoretical and practical : | 3,00 |

Type | Teacher | Classes | Hour |
---|---|---|---|

Theoretical and practical | Totals | 1 | 3,00 |

Ana Paula de Frias Viegas Proença Rocha | 0,00 |

To involve the students in a case study application for the practice and critical perception of the studied methods and underlying software packages and tools used.

The student should be able to:

- review essential discrete-time signal processing topics, including sampling effect and transforms

-characterize random signals in time, frequency and time-frequency/scale domain, formulate suitable models, estimate the parameters, and evaluate the quality of the estimates.

-use linear estimation theory, optimal linear estimation, Kalman and Wiener filtering, to solve estimation problems with applications in signal processing, such as telecommunications, bioengineering and telemedicine, but also in other disciplines such as finance and statistics..

- use adaptive signal processing algorithms for extracting relevant information from noisy signals: emphasis is on recursive, model based estimation methods for time-variant signals and systems and diverse case studies such as in finance and biomedical applications.

** -** critically select the methods and software tools for each concrete case study in broad multidisciplinary signal processing contexts with interpretation of the obtained results, by combining signal and data treatment

- Introduction to novel paradigms in statistical signal processing (selected topics following current interdisciplinary trends, centered in the different student profiles)

Random Processes. Characterization in time and frequency domains. Stationarity and ergodicity. Linear models. Spectral estimation. Parametric and non parametric methods. Introduction to time-frequency/scale analysis and wavelets. Optimal and adaptive signal processing fundamentals. Least mean squares and recursive algorithms.

Introduction to novel paradigms in statistical signal processing.

Applications/illustrations of the methods to case studies.

Trevor Hastie; The elements of statistical learning. ISBN: 978-0-387-84857-0

Semmlow John L.; Biosignal and medical image processing. ISBN: 9781466567368 (Biosignal and medical image processing / John L. Semmlow, Benjamin Griffel)

Vaseghi Saeed V.; Advanced digital signal processing and noise reduction. ISBN: 9780470754061 (Advanced digital signal processing and noise reduction / Saeed V. Vaseghi)

José Luis Rojo Álvarez; Digital signal processing with kernel methods. ISBN: 978-1-118-61179-1 (José Luis Rojo Álvarez;Digital signal processing with kernel methods. ISBN: 978-1-118-61179-1)

Ervin Sejdic, Tiago Falk; Signal processing and machine learning for biomedical big data, Taylor & Francis, 2018. ISBN: 978-1-4987-7345-4

Lectures TP to present and illustrate the topics. Problems / Projects with strong laboratorial computation component using Matlab (R/Python).

A special attention is given to the understanding of the concepts and methods with an effective use of simulated and experimental data. One of the aims of the discipline is also the improvement of the oral and written competences. The discipline presents an important computational component with MATLAB or other adequate computational environment

Matlab ( Toolbox Signal Processing)

R/Python

Matlab ( Toolbox Neural Networks and Deep Learning)

Physical sciences > Mathematics > Applied mathematics

Physical sciences > Mathematics > Applied mathematics > Engineering mathematics

designation | Weight (%) |
---|---|

Prova oral | 50,00 |

Trabalho escrito | 50,00 |

Total: |
100,00 |

designation | Time (hours) |
---|---|

Elaboração de projeto | 25,00 |

Estudo autónomo | 70,00 |

Frequência das aulas | 42,00 |

Trabalho laboratorial | 25,00 |

Total: |
162,00 |

Minimum of 8 on continous evaluation.

Minimum of 8 in individual project

Work / Labs (T-50%) and final Project (P-50%). The final Project evaluation, includes discussion (30%), final presentation (20%) and written report(50%).

Not applicable. Identical for all of the students.

Not applicable. Identical for all of the students.

Not applicable for the continous evaluation component T.

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Page created on: 2022-08-08 at 11:51:49 |**Reports Portal**

Page created on: 2022-08-08 at 11:51:49 |