Machine Learning for sensor data

Course outline

General concepts
  • Sampling theorem
  • Time windows and aggregation
  • Data splitting for time series data
  • Common data quality problems and how to solve them
  • Aligning flow by using dynamic time warping
  • Digital Twins
Pre-processing sensor data time series
  • Interpolation
  • Noise reduction
  • Outlier detection
  • Dimensionality reduction
  • Dealing with mixed sample frequencies
Feature calculation
  • Why?
  • Autocorrelation
  • Fourier transformations
  • Peak detection
  • ARIMA models
Forecasting  (on demand)
  • Arima models
  • Exponential smoothing models
  • Anomaly detection based on forecasting
Predictive maintenance (on demand)
  • Predictive maintenance and survival function estimation
  • Kaplan Meier estimators
  • Cox Proportional Hazard model
  • Aalen Additive Hazard model
  • Time series similarity kernel regression
Deep Learning for sensor data (on demand)
  • Recurrent architectures
  • Convolutional architectures
  • Anomaly detection based on auto-encoders
  • Reinforcement learning for optimal control
  • Differential evolution
  • Surrogate/Bayesian optimization

Case studies and exercises will take place per chapter.

The course outline will be tailored to the participant’s wishes and needs.


Vladimir Dzyuba, PhD

Senior ML Engineer

Jeroen Boeye

Head of Machine Learning

Course level


Admission Fee

€ 4500


  • Python programming at the intermediate level
  • Knowledge of basic Machine Learning concepts like data splitting, classification, overfitting, probabilities, ...

You get

Course material
Drinks, snacks and lunch
Cloud servers for use during training
4h of support and question answering up to 6 months after the course

Inquiry for your POC

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