The website of the Fundaci贸 Polit猫cnica de Catalunya uses its own and third-party cookies to improve the browsing experience and for statistical purposes. For more information about cookies you can consult the
Manage cookies
Allow all cookies
ABOUT
Cookies are small text files that can be used by websites to make a user's experience more efficient.
The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies we need your permission.
This site uses different types of cookies. Some cookies are placed by third party services that appear on our pages.
You can at any time change or withdraw your consent from the Cookie Declaration on our website.
Learn more about who we are, how you can contact us and how we process personal data in our .
When contacting us regarding your consent, please indicate your identification and the date of your consent.
COOKIE DECLARATION
Necessary
:
Necessary cookies help make a website usable by enabling basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies.
Name
Provider
Purpose
Expiry
Type
lang
www.fpc.upc.edu
Save the web display language
2 years
HTTP
politica_cookie
www.fpc.upc.edu
Save the general consent of cookies in the current domain.
1 year
HTTP
cookie_estadistiques
www.fpc.upc.edu
Save the consent of statistical cookies in the current domain.
1 year
HTTP
cookie_marketing
www.fpc.upc.edu
Save the consent of marketing cookies in the current domain.
1 year
HTTP
PHPSESSID
www.fpc.upc.edu
Preserves user session state across page requests.
厂别蝉蝉颈贸
HTTP
CONSENT [x3]
Used to detect if the visitor has accepted the marketing and analytics category in the cookie banner. This cookie is necessary for GDPR-compliance of the website.
2 years
HTTP
Statistics
:
Statistic cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously.
Name
Provider
Purpose
Expiry
Type
_ga
Registers a unique ID that is used to generate statistical data on how the visitor uses the website.
2 years
HTTP
_ga_#
Used by Google Analytics to collect data on the number of times a user has visited the website as well as dates for the first and most recent visit.
2 years
HTTP
_gat
Used by Google Analytics to throttle request rate
1 day
HTTP
_gid
Registers a unique ID that is used to generate statistical data on how the visitor uses the website.
1 day
HTTP
Marketing
:
Marketing cookies are used to track visitors across websites. The intention is to display ads that are relevant and engaging for the individual user and thereby more valuable for publishers and third party advertisers.
Name
Provider
Purpose
Expiry
Type
last_campaign
www.fpc.upc.edu
Save the last campaign for which the user has reached the website.
6 months
HTTP
campaign_XX
www.fpc.upc.edu
Save the last campaign for which the user has reached the website.
6 months
HTTP
ads/ga-audiences
Used by Google AdWords to re-engage visitors that are likely to convert to customers based on the visitor's online behaviour across websites. S
厂别蝉蝉颈贸
Pixel
NID
Registers a unique ID that identifies a returning user's device. The ID is used for targeted ads.
6 months
HTTP
bcookie
Used by the social networking service, LinkedIn, for tracking the use of embedded services.
2 years
HTTP
bscookie
Used by the social networking service, LinkedIn, for tracking the use of embedded services.
2 years
HTTP
lidc
Sets a unique ID for the visitor, that allows third party advertisers to target the visitor with relevant advertisement. This pairing service is provided by third party advertisement hubs, which facilitates real-time bidding for advertisers.
Session
Pixel
_fbp [x2]
Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers.
Machine and Deep Learning for the Detection of Anomalies in Images and Time Seriesconduct
Master AI to detect anomalies and deliver real solutions in key areas such as healthcare, energy, and industry.
Start date 30/09/2025
Duration 3 months
Delivery method Blended learning
Fee €900€270
Presentation
Training Content
Learning methodology
Teaching team
Funded by the Ministry of Science, Innovation and Universities within the framework of theMicrocreds Plan.Financing applied to residents in Spain between 25 and 64 years old.
This grant and UPC School discounts cannot be combined.
Presentation
Edition
1st
Credits
3 ECTS
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 theMicrocreds 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 programme or until end of vacancies.
EEBE - Escola d'Enginyeria de Barcelona Est
Avda. Eduard Maristany, 16 08019 Barcelona
Why this programme?
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:
芦Recovery, Transformation and Resilience Plan - Funded by the European Union - Next Generation EU禄. Component 21, investment 6, C21.I06.P02.S04.S05. PROVISIONAL.SI01.
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
Introduction and Core Problems:
General course introduction and objectives.
Case studies.
Definition of learning methodologies:
Supervised.
Unsupervised.
Semi-supervised.
Python and Google Colab:
Basic Python fundamentals:
Data types, structures, and control flow.
Functions and classes.
Handling essential libraries such as NumPy, Pandas, and Matplotlib.
Using Google Colab as a programming environment:
Basic setup and access to cloud resources.
Execution of notebooks and library management.
Data Processing:
Data acquisition:
Open data sources.
Data extraction and loading.
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.
Deep Learning:
Deep neural networks:
Multilayer Perceptron (MLP).
Recurrent Neural Networks (LSTM, GRU).
Advanced models:
Convolutional Neural Networks (CNN).
Vision Transformers (ViT).
General-purpose Transformers.
Final Projects:
Application of techniques to real-world cases.
Degree
Microcredential. Europass digital credential in Machine and Deep Learning for Image and Time Series Anomaly Detection issued by the Universitat Polit猫cnica de Catalunya.
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.
Virtual campus
The students on this microcredential will have access to the My_ Tech_Space virtual campus - an effective platform for work and communication between the programme's students, lecturers, directors and coordinators. My_Tech_Space provides the documentation for each training session before it starts, and enables students to work as a team, consult lecturers, check notes, etc.
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.
