Login

Fourth Machine Learning in High Energy Physics Summer School 2018

6–12 Aug 2018
University of Oxford
Europe/London timezone

Support

Session

Lectures

6 Aug 2018, 09:00
St Anne's College, Mary Ogilvie LT (University of Oxford)

St Anne's College, Mary Ogilvie LT

University of Oxford

56 Woodstock Rd, Oxford OX2 6HS, United Kingdom.

Conveners

Lectures: Day 1 Lectures (Track 1)

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

Lectures: Day 1 Lectures (Track 1)

  • Nikita Kazeev (Yandex School of Data Analysis (RU))

Lectures: Day 2 Lectures

  • Alexey Artemov (Yandex)

Lectures: Day 2 Lectures

  • Alexey Artemov (Yandex)

Lectures: Day 4 Lectures

  • Nikita Kazeev (Yandex School of Data Analysis (RU))

Lectures: Day 4 Lectures

  • Nikita Kazeev (Yandex School of Data Analysis (RU))

Lectures: Day 5 Lectures

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

Lectures: Day 5 Lectures

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

Lectures: Day 6 Lectures

  • Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

Lectures: Day 6 Lectures.

  • There are no conveners in this block

Lectures: Neural networks for Network structures (MPNN)

  • Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))

Presentation materials

There are no materials yet.

  1. 28. Track 1 Lecture. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
    Maxim Borisyak (Yandex School of Data Analysis (RU))
    06/08/2018, 09:00
  2. 30. Track 1 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
    06/08/2018, 13:30
  3. 10. Lecture. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.
    Alexey Artemov (Yandex)
    07/08/2018, 09:00
  4. 33. Lecture. Intro into Deep learning. Convolutional Neural Network.
    Alexey Artemov (Yandex)
    07/08/2018, 13:30
  5. 36. Lecture. Probability density estimation. Variational Autoencoder.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
    09/08/2018, 09:00
  6. 45. Lecture. Generative Adversarial Networks.
    Nikita Kazeev (Yandex School of Data Analysis (RU))
    09/08/2018, 13:30
  7. 54. Lecture. Black-box optimization: Bayesian Optimization, Variational Optimization.
    Maxim Borisyak (Yandex School of Data Analysis (RU))
    10/08/2018, 09:00
  8. 38. Lecture. Adversarial Optimization (in general), Adversarial Variational Optimization.
    Maxim Borisyak (Yandex School of Data Analysis (RU))
    10/08/2018, 13:30
  9. 40. Lecture. Domain Adaptation (reweighting, flatness, adversarial DA).
    Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
    11/08/2018, 09:00
  10. 56. Lecture. RNN: GRU + LSTM + Jet recognition.
    11/08/2018, 13:30
  11. 18. Boosting reweighting and flatness
  12. 19. Boosting reweighting and flatness - seminar
  13. 47. Certificates, Free Discussion
  14. 22. Deep learning I
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  15. 23. Deep learning II
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  16. 11. Ensembles

    bagging & boosting

    Go to contribution page
  17. 14. Feature engineering, Dimensionality reduction
  18. 15. Hyperparameters search

    Featuring Gaussian processes

    Go to contribution page
  19. 6. Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
  20. 34. Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.
  21. 42. Lecture 9. RNNs.
  22. 7. Metric ML algorithms

    SVM, KNN, Linear regression

    Go to contribution page
  23. 48. Track 2 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.
    Alexey Artemov (Yandex)
Building timetable...