Third Machine Learning in High Energy Physics Summer School 2017

Session

Seminars

17 Jul 2017, 11:10

Reading University (Reading)

Conveners

Seminars: Day 1 Seminars

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

Seminars: Day 1 Seminars

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

Seminars: Day 2 Seminars

• There are no conveners in this block

Seminars: Day 2 seminrs

• There are no conveners in this block

Seminars: Day 4 Seminars

• There are no conveners in this block

Seminars: Day 4 Seminars

• There are no conveners in this block

Seminars: Day 5 Seminars

• There are no conveners in this block

Seminars: Day 6 Seminars

• There are no conveners in this block

29. Seminar 1. Python data crunching: numpy, root_numpy, pandas (very short), matplotlib.

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

17/07/2017, 11:10

31. Seminar 2. Matplotlib, Trees & Linear Models in SciKit Learn. Overfitting checks & prevention.

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

17/07/2017, 15:50

32. Seminar 3. Ensembling: Adaboost, Stacking. Gradient Boosting.

18/07/2017, 11:00

13. Seminar 4. Optimization of hyper-parameters, applications to challenge.

18/07/2017, 15:30

37. Seminar 5. Computing gradient by hand. Tensorflow. Keras. Remote machine setup. Getting familiar with GPU.

20/07/2017, 11:00

46. Seminar 6. Image recognition using convolutional neural networks.

20/07/2017, 15:30

39. Seminar 7. PCA, Autoencoders (faces)

21/07/2017, 15:30

41. Seminar 8 Generative Adversarial Networks (physics)

22/07/2017, 11:00

8. Course technicalities, working environment

24. Deep learning III

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

25. Deep learning IV

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

27. Deep neural networks

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

16. Ensemble algorithms, dimensionality reduction

Random forest, gradient boosting, PCA

21. Feature engineering and selection

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

Flatness boosting and reweighting

17. Hyperparameters search

12. Model evaluation

Overfitting, Cross-validation

9. Python for data analysis; Probability density estimation

numpy, root_numpy, pandas, matplotlib

35. Seminar 6. Image recognition using convolutional neural networks.

20. Shallow neural networks

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