top of page

Anomaly Detection in Big Data

  • Leading institution: The Babes-Bolyai University

  • EC: 4

General Description

Anomaly Detection in Big Data' is a course designed to equip doctoral candidates with the principles and techniques necessary to identify anomalies within large datasets. Throughout this course, candidates will explore various methodologies for anomaly detection, including statistical approaches, machine learning algorithms, and advanced data mining techniques. Candidates will also gain experience in handling data errors, discussing strategies such as human inspection, outlier removal, and leveraging both traditional imputation techniques and artificial intelligence to fill data gaps. Furthermore, the course teaches how to map data quality metrics to assess the reliability and integrity of financial datasets, ensuring robust anomaly detection processes. By the end of this course, candidates will possess the skills and insights needed to effectively detect and mitigate anomalies in big data environments, enabling to safeguard against financial risks and improve decision-making processes.

unnamed.png

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Skłodowska-Curie Actions. Neither the European Union nor the granting authority can be held responsible for them. This project has received funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101119635

Follow us

  • Wikipedia
  • LinkedIn
logo-nobackground-500.png
FinAI_COST.png

© 2023-2024

bottom of page