Third Machine Learning in High Energy Physics Summer School 2017

Session

Lectures

17 Jul 2017, 09:00

Reading University (Reading)

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

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

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

10. Lecture 3. Ensembling: Adaboost, Stacking. Gradient Boosting.

18/07/2017, 09:00

33. Lecture 4. Hyperparameters optimization techniques and methods. Feature selection. TPE. Gaussian Processes

18/07/2017, 13:30

36. Lecture 5. Introduction into Neural Network models. Multi-layered Perceptron. Backpropagation.

20/07/2017, 09:00

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

20/07/2017, 13:30

38. Lecture 7. Dimensionality Reduction. PCA. LDA. LLE. TSNE. Autoencoders.

21/07/2017, 13:30

40. Lecture 8 Generative Adversarial Networks, Metric Learning.

22/07/2017, 09:00

42. Lecture 9. RNNs.

22/07/2017, 13:30

47. Certificates, Free Discussion

22/07/2017, 15:30

18. Boosting reweighting and flatness

19. Boosting reweighting and flatness - seminar

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.

7. Metric ML algorithms

SVM, KNN, Linear regression