Fifth Machine Learning in High Energy Physics Summer School 2019

Session

Lecture

5 Jul 2019, 09:00

SR4 (DESY)

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

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

18. Boosting reweighting and flatness

19. Boosting reweighting and flatness - seminar

47. Certificates, Free Discussion

22. Deep learning I

Mr Alexander Panin (Yandex School of Data Analysis (RU))

23. Deep learning II

Mr Alexander Panin (Yandex School of Data Analysis (RU))

11. Ensembles

bagging & boosting

14. Feature engineering, Dimensionality reduction

15. Hyperparameters search

Featuring Gaussian processes

6. Lecture 1. Introduction. Why ML works. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.

34. Lecture 6. Intro into Deep learning. Convolutional Neural Network. Model zoo. Augmentation.

42. Lecture 9. RNNs.

38. Lecture. Adversarial Optimization (in general), Adversarial Variational Optimization.

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

40. Lecture. Domain Adaptation (reweighting, flatness, adversarial DA).

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

45. Lecture. Generative Adversarial Networks.

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

33. Lecture. Intro into Deep learning. Convolutional Neural Network.

Alexey Artemov (Yandex)

10. Lecture. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.

Alexey Artemov (Yandex)

36. Lecture. Probability density estimation. Variational Autoencoder.

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

56. Lecture. RNN: GRU + LSTM + Jet recognition.

7. Metric ML algorithms

SVM, KNN, Linear regression

30. Track 1 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.

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

28. Track 1 Lecture. Overfitting. Model Selection. Figures of Merits. Linear Models. Regularization. Logistic Regression.

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

48. Track 2 Lecture. Ensembling: Stacking, RandomForest, AdaBoost, Gradient Boosting.

Alexey Artemov (Yandex)