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

The complete revision's draft of the Federal Data Protection Act is currently in political consultation. Data Protection is to be increased by giving people more control over their private data as well as reinforcing transparancy regarding the handling of confidential data.

Links: draft, report

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