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Page Content

WEB SESSION

5/6 Jun 2023

ML Explainability in Actuarial Data Science: A Practical Primer

Monday, 05 June 2023

  1. 09:00 - 10:30 Unsupervised Learning 1
  2. - Clustering: "How many clusters is the right answer?"
  3. 10:30 - 10:45 Break
  4. 10:45 - 12:15 Unsupervised Learning 1
  5. - Practice Session Clustering
  6. 12:15 - 13:15 Break
  7. 13:15 - 14:45 Unsupervised Learning 2
  8. - Dimensionality Reduction: The (auto)encoding perspective
  9. 14:45 - 15:00 Break
  10. 15:00 - 16:30 Unsupervised Learning 2
  11. - Practice Session Autoencoder

Tuesday, 06 June 2023

  1. 09:00 - 10:30 Supervised Learning 1
  2. - Surrogate models: Extending marginal projections
  3. - LIME and SHAP: Combinatorially exploring the model's feature space
  4. 10:30 - 10:45 Break
  5. 10:45 - 12:15 Supervised Learning 1
  6. - Practice Session LIME/SHAP
  7. 12:15 - 13:15 Break
  8. 13:15 - 14:45 Supervised Learning 2
  9. - Towards notions of confidence for distribution-free models: "Are residuals indicative without priors?"
  10. - Explaining Artificial Neural Nets
  11. 14:45 - 15:00 Break
  12. 15:00 - 16:30 Supervised Learning 2
  13. - Practice Session Neural Net Explainability
  14. .
  15. Preliminary programme. All the above times are given in CEST (Central European Time).
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