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Home   >  Master's & postgraduate courses  >  Education  >  Microcredential in Machine and Deep Learning for the Detection of Anomalies in Images and Time Seriesconduct
We advise you! Request information or admission
  • discount

    Funded by the Ministry of Science, Innovation and Universities within the framework of the Microcreds Plan. Financing applied to residents in Spain between 25 and 64 years old.

  • discount

    Free for people in a situation of unemployment, low income and risk of social exclusion within the framework of the Microcreds Plan.

These grants and UPC School discounts cannot be combined.

Presentation

Edition
1st Edition
Credits
3 ECTS (24 teaching hours)
Type
Microcredential
Delivery method
Blended learning
Language of instruction
Spanish
Fee
€900 €270(Funded by the Ministry of Science, Innovation and Universities within the framework of the Microcreds Plan. Financing applied to residents in Spain between 25 and 64 years old.)
Notes payment of enrolment fee and 0,7% campaign
Registration open until the beginning of the course or until end of vacancies.
Start date
Start date: 30/09/2025
End date: 16/12/2025
Timetable
Tuesday: 6:30 pm to 8:30 pm
- 4 sessions practical classroom
- 4 sessions theoretical classroom
- 8 sessions virtual connection
Taught at
EEBE - Escola d'Enginyeria de Barcelona Est
Avda. Eduard Maristany, 16
08019 Barcelona
Why this microcredential?
This microcredential offers specialized training in Machine Learning and Deep Learning techniques for anomaly detection, addressing the training needs of sectors such as biomedicine and wind energy. The learning experience combines theory and practice to equip participants with the skills to analyse complex data, such as biomedical images and time series, using artificial intelligence.

The Machine Learning and Deep Learning for Anomaly Detection in Images and Time Series course prepares participants to develop key competencies in data analysis and AI applied to anomaly detection.

Promoted by:
  • Ministerio de Ciencia, Innovaci贸n y Universidades
  • Plan de Recuperaci贸n, Transformaci贸n y Resiliencia Gobierno de Espa帽a
  • Uni贸n Europea. NextGenerationEU
Aims
  • Analyse data to identify patterns and anomalies.
  • Design, train, and evaluate machine learning models to detect anomalies in biomedical images and time series.
  • Detect anomalies by applying deep learning techniques using advanced architectures such as CNN and RNN.
  • Evaluate performance with appropriate metrics and interpret results for real-world application.
  • Integrate solutions into production environments, deploying anomaly detection models to ensure operational efficiency.
Who is it for?
Professionals from various sectors who need to apply advanced machine learning and deep learning techniques to detect anomalies. In particular, it focuses on the following areas:

  • Biomedicine and Health.
  • Wind Energy and Renewable Energies.
  • Manufacturing Industry

Training Content

List of subjects
3 ECTS 24h
+
Blended learning
Machine y Deep Learning para la Detecci贸n de Anomal铆as en Im谩genes y Series Temporales
Introduction and basic problems:

- General introduction to the course and objectives.
- Case studies.
- Definition of learning methodologies:
- Supervised.
- Unsupervised.
- Semi-supervised.

Python and Google Colab:

- Python basics:
- Data types, structures and control flow.
- Functions and classes.
- Handling of essential libraries such as NumPy, Pandas and Matplotlib.
- Use of Google Colab as a programming environment:
- Basic configuration and access to cloud resources.
- Execution of notebooks and handling of libraries.

3. Data processing:

- Data acquisition:
- Open data sources.
- Data extraction and loading.
- Data processing and cleaning:
- Identification and handling of missing values.
- Data normalization and standardization.
- Exploratory data analysis:
- Pattern visualization.
- Generation of statistical summaries.

Machine Learning:

- Supervised and unsupervised learning techniques

5. Deep Learning:

- Deep neural networks:
- Multilayer perceptron (MLP).
- Recurrent neural networks (LSTM, GRU).
- Advanced models:
- Convolutional Neural Networks (CNN).
- Vision Transformers (ViT).
- General transformers.

6. Final projects:

- Application of techniques in real cases.

The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the course objectives.
Degree

Europass digital credential issued by the Universitat Polit猫cnica de Catalunya in Machine and Deep Learning for Image and Time Series Anomaly Detection.

Learning methodology

The teaching methodology of the programme facilitates the student's learning and the achievement of the necessary competences.

- Problem-based learning.

- Project-based learning.

- Expository sessions of contents.

- Case studies.



Assessment criteria
Attendance
At least 80% attendance of teaching hours is required.
Work out projects
Studies on a specific topic, by individuals or groups, in which the quality and depth of the work is assessed, among other factors.

Teaching team

Academic management
  • Vidal Segui, Yolanda
    info
    / / /
    PhD in Applied Mathematics from the UPC, where she is associate professor and director of the research group Wind Turbine Condition Monitoring, integrated in CoDAlab ' Control, Data and Artificial Intelligence. Specialist in AI applied to wind turbine monitoring, she has led 4 competitive projects with outstanding collaborations with technology centers (Ikerlan) and companies in the wind energy sector. Senior member of the IEEE, she is the author of more than 65 articles in indexed journals, 11 books, 1 patent and more than 125 communications in conferences. In 2024 she received the 1st UPC Open Science Award.
Teaching staff
  • Vidal Segui, Yolanda
    info
    / / /
    PhD in Applied Mathematics from the UPC, where she is associate professor and director of the research group Wind Turbine Condition Monitoring, integrated in CoDAlab ' Control, Data and Artificial Intelligence. Specialist in AI applied to wind turbine monitoring, she has led 4 competitive projects with outstanding collaborations with technology centers (Ikerlan) and companies in the wind energy sector. Senior member of the IEEE, she is the author of more than 65 articles in indexed journals, 11 books, 1 patent and more than 125 communications in conferences. In 2024 she received the 1st UPC Open Science Award.

Request information or admission

Information and guidance:
Cecilia Salas Silva
(34) 93 706 80 35
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After we have registered your request, you will receive confirmation by email and we will be in touch.

Thank you for your interest in our training programmes.
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Name:

Course: Machine and Deep Learning for the Detection of Anomalies in Images and Time Seriesconduct

Fee: €900 €270(Funded by the Ministry of Science, Innovation and Universities within the framework of the Microcreds Plan. Financing applied to residents in Spain between 25 and 64 years old.)

Submit and make the payment
  • If you have any doubts.
  • If you want to start the registration procedure.
How to start admission
To familiarise yourself with the registration process for this course, please contact:

Cecilia Salas Silva
(34) 93 706 80 35
cecilia.salas@talent.upc.edu




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ACADEMIC AND ECONOMIC REGULATIONS

The Fundaci贸 Polit猫cnica de Catalunya reserves the right to modify content, price, location, timetable and dates prior to the start of the course. Registration on the course will not be confirmed until payment has been made.

Registration rights. The interested party must make payment of the specified registration fee for the course. This fee will be deducted from the full course fee and will only be reimbursed if the applicant is not admitted.

Cancellation or deferment.The Fundaci贸 Polit猫cnica de Catalunya reserves the right to cancel or defer a course if the minimum number of students is not met. In case of cancellation or non-admittance, the Fundaci贸 Polit猫cnica de Catalunya will return all amounts paid in full, without any additional compensation. In case of deferment, applicants may request reimbursement of fees paid.

Cancellation of registration.
In the event of withdrawal or cancellation of the registration, the student must notify the UPC School in writing beforehand.
  • If this request for cancellation is made 45 days before the start of the programme, the UPC School will retain only 30% of the total registration fee and refund the difference paid.
  • In the event that the application request is made within 45 calendar days and the beginning of the programme, the UPC School will retain 60% of the registration fee.
  • No applications for refunds may be made after the programme has started.
Under exceptional circumstances, refunds of the registration fee will be made if the student's cancellation is due to one of the following circumstances:
  • Denial of a visa, subject to submission of supporting documentation. In this case, the UPC School will refund the registration fee less 300 Euros for administrative expenses.
  • Serious illness or accident accredited by an official medical certificate, stating the start date of the illness and the anticipated convalescence period. In this situation the UPC School's decision will be as follows:
    • If the notification takes place up to one month after the start of the programme, it will refund the amount actually paid, less 300 Euros as administrative expenses.
    • No refunds will be made after a month after the start of the programme. It will be only be possible to use the amount paid as a deposit for the registration fee of the next programme. This procedure entails no administrative fee for the student. The price difference between the new registration fee and the amount previously paid will be payable by the student under all circumstances.

Changes in registration. Any changes in registration, previously authorised by the Fundaci贸 Polit猫cnica de Catalunya, will incur a 300 € administration fee.

Discounts.
  • Discounts are non-accumulable. The greater discount of those requested will be applied.
  • Discounts can only be applied under prior application and approval.
  • Once registration has been confirmed, no discount will be applied.
  • Students are responsible for placing applications for any discounts.

Qualification. In order to obtain the Qualification/Diploma issued by the Polytechnic University of Catalonia, the student must be in possession of a recognised university qualification or internal university qualification equivalent to a degree or diploma. If this is not the case, the student will receive a certificate of completion for the course, issued by the Fundaci贸 Polit猫cnica de Catalunya. Students with outstanding payments due to the Fundaci贸 Polit猫cnica de Catalunya or who has not approved all the credits necessary to overcome the program before the date of completion of this program will not be eligible to receive any qualification, diploma or certificate.

Barcelona, October 31, 2017


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