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 conceptsย
- 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 Twinsย
Pre-processing sensor data time seriesย
- Interpolation
- Noise reductionย
- Outlier detection
- Dimensionality reduction
- Dealing with mixed sample frequenciesย
Feature calculationย
- Why?
- Autocorrelation
- Fourier transformations
- Peak detection
- ARIMA models
Forecastingย (on demand)
- Arima models
- Exponential smoothing models
- Anomaly detection based on forecastingย
Predictive maintenance (on demand)
- Predictive maintenance and survival function estimationย
- Kaplan Meier estimators
- Cox Proportional Hazard model
- Aalen Additive Hazard model
- Time series similarity kernel regressionย
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 optimizationย
Case studies and exercises will take place per chapter.
The course outline will be tailored to the participant’s wishes and needs.ย
Course level
Expert
Prerequisites
- Python programming at the intermediate level
- Knowledge of basic Machine Learning concepts like data splitting, classification, overfitting, probabilities, ...
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
- 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
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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.
