14 hours – 2 days.
- Familiarity with college-level mathematics (probability, statistics, calculus).
- Familiarity with Python programming.
Sensor data are everywhere: from electrocardiograms to infrared sensors measuring nutrient content in food. For some applications there is either way too little data (100-150 points in chemometrics) which makes classification and regression hard. For some other problems, there is too much data (sensor measurements) which makes indexing and retrieval hard. Out of the box machine learning require significant tweaks to be effective. In this course we introduce and motivate some of those tweaks.
- Introduction. Similarity measures for sensor data.
- Clustering and indexing of high dimensional data.
- Classification and regression. Feature extraction.
- Anomaly and change detection.
- 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.
300 EUR/person + VAT.
Note: The course is designed to provide a broad overview. However, we can customize the course to cover your specific needs.