Fourth Machine Learning in High Energy Physics Summer School 2018

Session

Lectures

6 Aug 2018, 09:00

St Anne's College, Mary Ogilvie LT (University of Oxford)

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))

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

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

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

06/08/2018, 13:30

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

Alexey Artemov (Yandex)

07/08/2018, 09:00

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

Alexey Artemov (Yandex)

07/08/2018, 13:30

36. Lecture. Probability density estimation. Variational Autoencoder.

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

09/08/2018, 09:00

45. Lecture. Generative Adversarial Networks.

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

09/08/2018, 13:30

54. Lecture. Black-box optimization: Bayesian Optimization, Variational Optimization.

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

10/08/2018, 09:00

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

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

10/08/2018, 13:30

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

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

11/08/2018, 09:00

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

11/08/2018, 13:30

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.

7. Metric ML algorithms

SVM, KNN, Linear regression

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

Alexey Artemov (Yandex)