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MLHEP 2015 summer school

27–30 Aug 2015
Academic University
Europe/Moscow timezone

Contribution List

38 out of 38 displayed
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  1. 37. Welcome to MLHEP
    27/08/2015, 09:00
  2. 38. MLHEP Kaggle competition
    Nikita Kazeev (Yandex School of Data Analysis (RU))
    27/08/2015, 09:20
  3. 3. Intro: General pipeline, ML at a glance
    27/08/2015, 09:40
  4. 5. Reminder about major algorithms. Advanced aspects of their use. Model evaluation.
    27/08/2015, 09:40
  5. 6. Feature selection. Regularization.
    27/08/2015, 11:20
  6. 4. Intro: kNN, basic overfitting, roc curve, logistic regression
    27/08/2015, 11:20
  7. 7. Introduction, course technicalities
    27/08/2015, 13:45
  8. 9. MLHEP introduction. ML vs MLHEP: correlation and agreement restrictions
    Tatiana Likhomanenko (National Research Centre Kurchatov Institute (RU))
    27/08/2015, 13:45
  9. 8. NumPy, Pandas, Matplotlib
    27/08/2015, 15:40
  10. 10. sPlot technique. ML on sPlot data.
    27/08/2015, 15:40
  11. 1. Applying Machine Learning to LHC triggers optimization
    Mika Anton Vesterinen (Ruprecht-Karls-Universitaet Heidelberg (DE))
    27/08/2015, 17:20
  12. 13. model ensembling #1
    28/08/2015, 09:30
  13. 11. neural networks
    28/08/2015, 09:30
  14. 12. decision tree, regression tree
    28/08/2015, 11:10
  15. 14. model ensembling #2
    28/08/2015, 11:10
  16. 17. Brave new boosting world: flatness boosting
    28/08/2015, 13:45
  17. 15. Overfitting, Cross-validation
    28/08/2015, 13:45
  18. 18. Brave new boosting world: reweighing boosting. Iterative learning
    28/08/2015, 15:40
  19. 16. Sklearn: KNN, decision tree, logistic regression
    28/08/2015, 15:40
  20. 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
  21. 20. Ensembles
    29/08/2015, 09:30
  22. 22. Linear dimensionality reduction.
    29/08/2015, 09:30
  23. 21. Gradient boosting; boosting to uniformity.
    29/08/2015, 11:10
  24. 23. Non-linear dimensionality reduction. Kernel trick. Common kernels.
    29/08/2015, 11:10
  25. 24. Ensemble algorithms: random forest, gradient boosting
    29/08/2015, 13:45
  26. 26. Meta Algorithms Applications in HEP.
    29/08/2015, 13:45
  27. 25. Hyperparam Optimization
    29/08/2015, 15:40
  28. 27. Hypotheses testing.
    29/08/2015, 15:40
  29. 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
  30. 29. GBDT tuning, reweighting, testing hypotheses, gaussian processes for optimization
    30/08/2015, 09:00
  31. 31. Kernelized algorithms: SVM, regression, K-NN, PCA and their properties.
    30/08/2015, 09:00
  32. 32. Deep learning. Different kinds of learning.
    30/08/2015, 10:40
  33. 30. Unsupervised learning, Latent Dirichlet Allocation, PCA. Fast predictions and pruning
    30/08/2015, 10:40
  34. 33. PCA, RBM and alike on MNIST
    30/08/2015, 13:00
  35. 35. Summary: HEP ML analysis sketch, from data to discovery.
    30/08/2015, 13:00
  36. 34. Clustering
    30/08/2015, 14:45
  37. 36. My own first NN using theano.
    30/08/2015, 14:45
  38. 28. Multivariate techniques in the Higgs search
    Josh Bendavid (CERN)
    30/08/2015, 16:20

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