Faktion Academy.

Machine Learning for sensor data

This course will teach best practices on deploying ML models, model management, data versioning and useful frameworks to use.

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. 

Course level



Course teachers

Jeroen Boeye, PhD
Head of Sensor Data

Vladimir Dzyuba, PhD
Senior ML Engineer

Course fee

EUR 3.000 excl. VAT

Included in course package

The Natural Language Processing course was a perfect balance between a summary view of the NLP journey with its modelling approaches over time and some technical details, plus a practical business view on real-world applications.

Claudia Burgard

Senior Data Scientist at Stepstone

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Machine Learning for Sensor Data


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