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Course unit, curriculum year 2024–2025
DATA.ML.450
Time Series Analysis using Machine Learning, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 1 (1.8.2024–20.10.2024)
Active in period 2 (21.10.2024–31.12.2024)
Course code
DATA.ML.450Language of instruction
EnglishAcademic years
2024–2025, 2025–2026, 2026–2027Level of study
Intermediate studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Jari TurunenResponsible teacher:
Tarmo LippingResponsible teacher:
Juho KanniainenResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
Common learning outcomes
Ethics
Core content
- Reduction of noise in time series, different filtering options, outlier processing
- Understanding
the concept of internal dependencies (correlation) of the time
series, understanding the dependencies between several types of
measurements
- Automatic phenomena extraction from data and isolation of significant data from time series
- Feature extraction from time series
- Prediction of data by using linear and machine learning methods, estimation of missing data
Complementary content
- Visualization of the phenomena both in time and frequency domains
- Implementation of correlation search between the measurement sets.
- Understand how phenomena can be searched automatically
- Model selection
Special knowledge
- Understanding the Dependence Between Time and Frequency Domains
- Good knowledge of machine learning models, finetuning
Learning outcomes
Prerequisites
Recommended prerequisites
Further information
Learning material
Equivalences
Studies that include this course
Completion option 1
All assignments must be done within the same semester
Independent study
30.08.2024 – 31.12.2024
Active in period 1 (1.8.2024–20.10.2024)
Active in period 2 (21.10.2024–31.12.2024)