Things to know about machine learning (ML)

Machine learning is widely used in computer science and other fields such as compliance. Developing successful machine learning applications, however, requires a substantial amount of "black art" that is difficult to find in textbooks. Here come the 12 key lessons that machine learning researches have learned.

  1. Learning = Representation + Evaluation + Optimization.
  2. It is Generalization that Counts.
  3. Data Alone Is Not Enough.
  4. Overfitting Has Many Faces.
  5. Intuition Fails in High Dimensions.
  6. Theoretical Guarantees Are Not What They Seem.
  7. Feature Engineering Is The Key.
  8. More Data Beats a Cleverer Algorithm.
  9. Learn Many Models, Not just One.
  10. Simplicity Does Not Imply Accuracy.
  11. Representable Does Not Imply Learnable.
  12. Correlation Does Not Imply Causation.

Complete Revision of the Federal Data Protection Act

Complete Revision of the Federal Data Protection Act: „As of 15th September 2017, draft and report for a completely revised Federal Data Protection Act is public. In a first step parliament and the people agreed to adaptations in order to be compliant with EU law. The second part of the revision is debated by the parliament since September 2019. Data Protection is to be increased by giving people more control over their private data as well as reinforcing transparency regarding the handling of confidential data.”


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