Checking date: 26/04/2024

Course: 2024/2025

Machine Learning in TImeseries and Data Streams
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)


Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


The aim of the course is to provide the student with the necessary knowledge on the application of machine learning techniques in both time series and continuous data streams. The student will learn the fundamental principles of the application of machine learning in this type of data.
Skills and learning outcomes
Description of contents: programme
The programme of this subject is divided into two clearly defined parts: Time series: - Overview of time series. - Data preparation. - Autoregressive and automated methods. - Supervised learning techniques for time series. - Applications. Incremental learning: - Overview of incremental learning. - Concept Drift. - Supervised incremental learning. - Unsupervised incremental learning. - Applications
Learning activities and methodology
Training Activities: -------------------------- AF1 - Theoretical class AF3 - Theoretical-practical classes AF5 - Individual and group tutorials AF6 - Group work AF7 - Individual student work Teaching methodology: ------------------------ MD1: Class lectures by the lecturer with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2: Critical reading of texts recommended by the subject teacher: press articles, reports, manuals and/or academic articles, either for subsequent discussion in class or to expand and consolidate knowledge of the subject. 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 teacher, of topics related to the content of the subject, as well as practical cases. MD5: Preparation of individual or group work and reports.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80

Calendar of Continuous assessment

Basic Bibliography
  • Bifet, Albert. Machine Learning for Data Streams: with Practical Examples in MOA. Cambridge: MIT Press. 2018
  • Bifet, Albert ; Gavaldà, Ricard ; Holmes, Geoff ; Pfahringer, Bernhard. Machine Learning for Data Streams: with Practical Examples in MOA. Cambridge: MIT Press. 2018
  • Konar, Amit ; Bhattacharya, Diptendu. Time-Series Prediction and Applications: A Machine Intelligence Approach. Springer International Publishing. 2017
  • Lazzeri, Francesca. Machine Learning for Time Series Forecasting with Python. Newark: John Wiley & Sons. 2020
  • Moamar Sayed-Mouchaweh editor.. Learning from Data Streams in Evolving Environments Methods and Applications. Cham: Springer International Publishing. 2019

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