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Fifth Machine Learning in High Energy Physics Summer School 2019

1–10 Jul 2019
DESY
Europe/Berlin timezone

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Session

Lecture

5 Jul 2019, 09:00
SR4 (DESY)

SR4

DESY

Notkestr. 85, 22607 Hamburg, Germany

Conveners

Lecture: Generative Models (GM); GMM; Probability density estimation (kernel, fitting); RBM; JS GAN; pivot

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

Presentation materials

generative.pdf
  1. 54. Lecture: Generative Models (GM); GMM; Probability density estimation (kernel, fitting); RBM; JS GAN; pivot
    Maxim Borisyak (Yandex School of Data Analysis (RU))
    05/07/2019, 09:00
  2. 18. Boosting reweighting and flatness
  3. 19. Boosting reweighting and flatness - seminar
  4. 47. Certificates, Free Discussion
  5. 22. Deep learning I
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  6. 23. Deep learning II
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  7. 11. Ensembles

    bagging & boosting

    Go to contribution page
  8. 14. Feature engineering, Dimensionality reduction
  9. 15. Hyperparameters search

    Featuring Gaussian processes

    Go to contribution page
  10. 6. Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
  11. 34. Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.
  12. 42. Lecture 9. RNNs.
  13. 38. Lecture. Adversarial Optimization (in general), Adversarial Variational Optimization.
    Maxim Borisyak (Yandex School of Data Analysis (RU))
  14. 40. Lecture. Domain Adaptation (reweighting, flatness, adversarial DA).
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
  15. 45. Lecture. Generative Adversarial Networks.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
  16. 33. Lecture. Intro into Deep learning. Convolutional Neural Network.
    Alexey Artemov (Yandex)
  17. 10. Lecture. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.
    Alexey Artemov (Yandex)
  18. 36. Lecture. Probability density estimation. Variational Autoencoder.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
  19. 56. Lecture. RNN: GRU + LSTM + Jet recognition.
  20. 7. Metric ML algorithms

    SVM, KNN, Linear regression

    Go to contribution page
  21. 30. Track 1 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
  22. 28. Track 1 Lecture. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
    Maxim Borisyak (Yandex School of Data Analysis (RU))
  23. 48. Track 2 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.
    Alexey Artemov (Yandex)
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