Login

Fifth Machine Learning in High Energy Physics Summer School 2019

1–10 Jul 2019
DESY
Europe/Berlin timezone

Support

Session

Seminar

1 Jul 2019, 14:00
SR4 (DESY)

SR4

DESY

Notkestr. 85, 22607 Hamburg, Germany

Conveners

Seminar: Figures of Merit, overfitting (MLE vs MAP vs AP)

  • Alexey Artemov (Yandex)

Seminar: Learning to pivot

  • Maxim Borisyak (Yandex School of Data Analysis (RU))

Seminar: NN interpretation. Tips & tweaks.

  • Maxim Borisyak (Yandex School of Data Analysis (RU))

Presentation materials

learning-to-pivot.pdf
  1. 31. Seminar: Figures of Merit, overfitting (MLE vs MAP vs AP)
    Dr Alexey Artemov (Skoltech, HSE)
    01/07/2019, 14:00
  2. 55. Seminar: Learning to pivot
    Maxim Borisyak (Yandex School of Data Analysis (RU))
    05/07/2019, 11:00
  3. 39. Seminar: NN interpretation. Tips & tweaks.
    Alexander Panin (Yandex School of Data Analysis (RU)), Maxim Borisyak (Yandex School of Data Analysis (RU))
    05/07/2019, 17:15
  4. 8. Course technicalities, working environment
  5. 24. Deep learning III
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  6. 25. Deep learning IV
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  7. 27. Deep neural networks
    Alexander Panin (Yandex School of Data Analysis (RU))
  8. 16. Ensemble algorithms, dimensionality reduction

    Random forest, gradient boosting, PCA

    Go to contribution page
  9. 21. Feature engineering and selection
    Nikita Kazeev (Yandex School of Data Analysis (RU))

    Flatness boosting and reweighting

    Go to contribution page
  10. 17. Hyperparameters search
  11. 12. Model evaluation

    Overfitting, Cross-validation

    Go to contribution page
  12. 9. Python for data analysis; Probability density estimation

    numpy, root_numpy, pandas, matplotlib

    Go to contribution page
  13. 35. Seminar 6. Image recognition using convolutional neural networks.
  14. 32. Seminar. Computing gradient by hand. Keras.
    Alexey Artemov (Yandex)
  15. 46. Seminar. Generative Adversarial Networks.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
  16. 53. Seminar. Graph Models (MPNN).
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
  17. 13. Seminar. Image recognition using convolutional neural networks.
    Alexey Artemov (Yandex)
  18. 41. Seminar. NN interpretation. Tips & tweaks.
    Dr Alexey Artemov (Skoltech, HSE)
  19. 37. Seminar. Tensorflow, Variational Autoencoder.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
  20. 20. Shallow neural networks
    Alexander Panin (Yandex School of Data Analysis (RU))
  21. 29. Track 1 Seminar. Introduction into Decision Trees. Python ML stack recap (numpy, scikit-learn, pandas).
    Maxim Borisyak (Yandex School of Data Analysis (RU))
Building timetable...