MLHEP 2015 summer school

Contribution List

37. Welcome to MLHEP

27/08/2015, 09:00

Welcome

38. MLHEP Kaggle competition

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

27/08/2015, 09:20

Welcome

3. Intro: General pipeline, ML at a glance

27/08/2015, 09:40

Introductory track

5. Reminder about major algorithms. Advanced aspects of their use. Model evaluation.

27/08/2015, 09:40

Advanced track

6. Feature selection. Regularization.

27/08/2015, 11:20

Advanced track

4. Intro: kNN, basic overfitting, roc curve, logistic regression

27/08/2015, 11:20

Introductory track

7. Introduction, course technicalities

27/08/2015, 13:45

Introductory track

9. MLHEP introduction. ML vs MLHEP: correlation and agreement restrictions

Tatiana Likhomanenko (National Research Centre Kurchatov Institute (RU))

27/08/2015, 13:45

Advanced track

8. NumPy, Pandas, Matplotlib

27/08/2015, 15:40

Introductory track

10. sPlot technique. ML on sPlot data.

27/08/2015, 15:40

Advanced track

1. Applying Machine Learning to LHC triggers optimization

Mika Anton Vesterinen (Ruprecht-Karls-Universitaet Heidelberg (DE))

27/08/2015, 17:20

13. model ensembling #1

28/08/2015, 09:30

Advanced track

11. neural networks

28/08/2015, 09:30

Introductory track

12. decision tree, regression tree

28/08/2015, 11:10

Introductory track

14. model ensembling #2

28/08/2015, 11:10

Advanced track

17. Brave new boosting world: flatness boosting

28/08/2015, 13:45

Advanced track

15. Overfitting, Cross-validation

28/08/2015, 13:45

Introductory track

18. Brave new boosting world: reweighing boosting. Iterative learning

28/08/2015, 15:40

Advanced track

16. Sklearn: KNN, decision tree, logistic regression

28/08/2015, 15:40

Introductory track

2. Challenges of searching for physics signatures with a large amount of background

Patrick Haworth Owen (Imperial College Sci., Tech. & Med. (GB))

28/08/2015, 17:30

20. Ensembles

29/08/2015, 09:30

Introductory track

22. Linear dimensionality reduction.

29/08/2015, 09:30

Advanced track

21. Gradient boosting; boosting to uniformity.

29/08/2015, 11:10

Introductory track

23. Non-linear dimensionality reduction. Kernel trick. Common kernels.

29/08/2015, 11:10

Advanced track

24. Ensemble algorithms: random forest, gradient boosting

29/08/2015, 13:45

Introductory track

26. Meta Algorithms Applications in HEP.

29/08/2015, 13:45

Advanced track

25. Hyperparam Optimization

29/08/2015, 15:40

Introductory track

27. Hypotheses testing.

29/08/2015, 15:40

Advanced track

19. MVA vs cut-and-count techniques in the SM model processes searches and measurements

Lesya Shchutska (University of Florida (US))

29/08/2015, 17:30

29. GBDT tuning, reweighting, testing hypotheses, gaussian processes for optimization

30/08/2015, 09:00

Introductory track

31. Kernelized algorithms: SVM, regression, K-NN, PCA and their properties.

30/08/2015, 09:00

Advanced track

32. Deep learning. Different kinds of learning.

30/08/2015, 10:40

Advanced track

30. Unsupervised learning, Latent Dirichlet Allocation, PCA. Fast predictions and pruning

30/08/2015, 10:40

Introductory track

33. PCA, RBM and alike on MNIST

30/08/2015, 13:00

Introductory track

35. Summary: HEP ML analysis sketch, from data to discovery.

30/08/2015, 13:00

Advanced track

34. Clustering

30/08/2015, 14:45

Introductory track

36. My own first NN using theano.

30/08/2015, 14:45

Advanced track

28. Multivariate techniques in the Higgs search

Josh Bendavid (CERN)

30/08/2015, 16:20

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