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Third Machine Learning in High Energy Physics Summer School 2017

17–23 Jul 2017
Reading
Europe/London timezone

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Session

Lectures

17 Jul 2017, 09:00
Reading University (Reading)

Reading University

Reading

Reading University, Reading, United Kingdom

Conveners

Lectures: Day 1 Lectures

  • Alexey Artemov (Yandex)

Lectures: Day 1 Lectures

  • Alexey Artemov (Yandex)

Lectures: Day 2 Lectures

  • There are no conveners in this block

Lectures: Day 2 Lectures

  • There are no conveners in this block

Lectures: Day 4 Lectures

  • There are no conveners in this block

Lectures: Day 4 Lectures

  • There are no conveners in this block

Lectures: Day 5 Lectures

  • There are no conveners in this block

Lectures: Day 6 Lectures

  • There are no conveners in this block

Lectures: Day 6 Lectures

  • There are no conveners in this block

Presentation materials

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  1. 28. Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
    Alexey Artemov (Yandex)
    17/07/2017, 09:00
  2. 30. Lecture 2. Decision Trees. Bagging. Ensembles. RandomForest. AdaBoost. GB-Reweighting. Gradient Boosting. Decorrelation of features with predictions.
    Alexey Artemov (Yandex)
    17/07/2017, 14:00
  3. 10. Lecture 3. Ensembling: Adaboost, Stacking. Gradient Boosting.
    18/07/2017, 09:00
  4. 33. Lecture 4. Hyperparameters optimization techniques and methods. Feature selection. TPE. Gaussian Processes
    18/07/2017, 13:30
  5. 36. Lecture 5. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.
    20/07/2017, 09:00
  6. 45. Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.
    20/07/2017, 13:30
  7. 38. Lecture 7. Dimensionality Reduction. PCA. LDA. LLE. TSNE. Autoencoders.
    21/07/2017, 13:30
  8. 40. Lecture 8 Generative Adversarial Networks, Metric Learning.
    22/07/2017, 09:00
  9. 42. Lecture 9. RNNs.
    22/07/2017, 13:30
  10. 47. Certificates, Free Discussion
    22/07/2017, 15:30
  11. 18. Boosting reweighting and flatness
  12. 19. Boosting reweighting and flatness - seminar
  13. 22. Deep learning I
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  14. 23. Deep learning II
    Mr Alexander Panin (Yandex School of Data Analysis (RU))
  15. 11. Ensembles

    bagging & boosting

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  16. 14. Feature engineering, Dimensionality reduction
  17. 15. Hyperparameters search

    Featuring Gaussian processes

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  18. 6. Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.
  19. 34. Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.
  20. 7. Metric ML algorithms

    SVM, KNN, Linear regression

    Go to contribution page
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