08/09/2020
10/09/2020

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.

Teachers

Vladimir Dzyuba, PhD

Senior ML Engineer
Vladimir_Dzyuba-IMG_0763

Jeroen Boeye, PhD

Head of Machine Learning

Jeroen is leading the Machine Learning team on a mission to bring value to our customers using data. The team uses Computer Vision, Reinforcement Learning, and Natural Language Processing to reach that goal.

Jeroen_Boeye-IMG_6370

Course level

expert

Admission Fee

€ 4500

Prerequisites

  • 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
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