21 hours – 3 days.
- Familiarity with college-level mathematics (probability, statistics, calculus).
- Familiarity with Python programming.
Time series data are important for economic forecasting and financial modelling. However, traditional methods in econometrics are hard to adapt to non-stationary time series and to additional metadata that might affect the quality of the forecasting. In this course we will show how to use machine learning for these tasks, as well as classification, regression and clustering of time series.
- Introduction to machine learning.
- Supervised and unsupervised learning.
- Similarity measures for time series data.
- Clustering and indexing of high dimensional data.
- Classification and regression. Feature extraction.
- Anomaly and change detection. Forecasting.
- Analysts, engineers, developers and machine learning practitioners.
Format of the course
- This is a hands-on course, with live coding and exercises. Participants should bring their own laptops.
450 EUR/person + VAT.
Note: The course is designed to provide a broad overview. However, we can customize the course to cover your specific needs.