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08:00
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Registration
(until 08:15)
()
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08:15
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--- breakfast ---
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09:00
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Welcome
-
Andrey Ustyuzhanin
(Yandex School of Data Analysis (RU))
(until 09:35)
(204)
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09:00
|
Welcome to MLHEP
(204)
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09:20
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MLHEP Kaggle competition
-
Nikita Kazeev
(Yandex School of Data Analysis (RU))
(204)
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09:35
|
--- break ---
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09:40
|
Advanced track
-Dr
Victor Kitov
(MSU)
(until 12:40)
(206)
|
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09:40
|
Reminder about major algorithms. Advanced aspects of their use. Model evaluation.
(206)
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11:00
|
--- coffee break ---
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11:20
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Feature selection. Regularization.
(206)
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09:40
|
Introductory track
-
Alexey Rogozhnikov
(until 12:40)
(204)
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09:40
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Intro: General pipeline, ML at a glance
(204)
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11:00
|
--- coffee break ---
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11:20
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Intro: kNN, basic overfitting, roc curve, logistic regression
(204)
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|
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08:15
|
--- breakfast ---
|
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09:30
|
Advanced track
-Dr
Victor Kitov
(MSU)
(until 12:30)
(206)
|
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09:30
|
model ensembling #1
(206)
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10:50
|
--- coffee break ---
|
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11:10
|
model ensembling #2
(206)
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09:30
|
Introductory track
-
Alexey Rogozhnikov
(until 12:30)
(204)
|
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09:30
|
neural networks
(204)
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10:50
|
--- coffee break ---
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11:10
|
decision tree, regression tree
(204)
|
|
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08:15
|
--- breakfast ---
|
|
09:30
|
Advanced track
-Dr
Victor Kitov
(MSU)
(until 12:30)
(206)
|
|
09:30
|
Linear dimensionality reduction.
(206)
|
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10:50
|
--- coffee break ---
|
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11:10
|
Non-linear dimensionality reduction. Kernel trick. Common kernels.
(206)
|
|
09:30
|
Introductory track
-
Alexey Rogozhnikov
(until 12:30)
(204)
|
|
09:30
|
Ensembles
(204)
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10:50
|
--- coffee break ---
|
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11:10
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Gradient boosting; boosting to uniformity.
(204)
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|
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08:15
|
--- breakfast ---
|
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09:00
|
Advanced track
-Dr
Victor Kitov
(MSU)
(until 12:00)
(206)
|
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09:00
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Kernelized algorithms: SVM, regression, K-NN, PCA and their properties.
(206)
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10:20
|
--- coffee break ---
|
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10:40
|
Deep learning. Different kinds of learning.
(206)
|
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09:00
|
Introductory track
-
Alexey Rogozhnikov
(until 12:00)
(204)
|
|
09:00
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GBDT tuning, reweighting, testing hypotheses, gaussian processes for optimization
(204)
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10:20
|
--- coffee break ---
|
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10:40
|
Unsupervised learning, Latent Dirichlet Allocation, PCA. Fast predictions and pruning
(204)
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|
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12:40
|
--- lunch ---
|
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13:45
|
Advanced track
-
Tatiana Likhomanenko
(National Research Centre Kurchatov Institute (RU))
(until 17:10)
(206)
|
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13:45
|
MLHEP introduction. ML vs MLHEP: correlation and agreement restrictions
-
Tatiana Likhomanenko
(National Research Centre Kurchatov Institute (RU))
(206)
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15:15
|
--- coffee break ---
|
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15:40
|
sPlot technique. ML on sPlot data.
(206)
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13:45
|
Introductory track
-
Nikita Kazeev
(Yandex School of Data Analysis (RU))
(until 17:10)
(204)
|
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13:45
|
Introduction, course technicalities
(204)
|
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15:15
|
--- coffee break ---
|
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15:40
|
NumPy, Pandas, Matplotlib
(204)
|
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17:10
|
--- break ---
|
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17:20
|
Applying Machine Learning to LHC triggers optimization
-
Mika Anton Vesterinen
(Ruprecht-Karls-Universitaet Heidelberg (DE))
(204)
|
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19:00
|
--- Welcome dinner ---
|
|
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12:30
|
--- lunch ---
|
|
13:45
|
Advanced track
-
Tatiana Likhomanenko
(National Research Centre Kurchatov Institute (RU))
(until 17:10)
(206)
|
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13:45
|
Brave new boosting world: flatness boosting
(206)
|
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15:15
|
--- coffee break ---
|
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15:40
|
Brave new boosting world: reweighing boosting. Iterative learning
(206)
|
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13:45
|
Introductory track
-
Nikita Kazeev
(Yandex School of Data Analysis (RU))
(until 17:10)
(204)
|
|
13:45
|
Overfitting, Cross-validation
(204)
|
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15:15
|
--- coffee break ---
|
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15:40
|
Sklearn: KNN, decision tree, logistic regression
(204)
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17:10
|
--- break ---
|
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17:30
|
Challenges of searching for physics signatures with a large amount of background
-
Patrick Haworth Owen
(Imperial College Sci., Tech. & Med. (GB))
(204)
|
|
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12:30
|
--- lunch ---
|
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13:45
|
Advanced track
-
Tatiana Likhomanenko
(National Research Centre Kurchatov Institute (RU))
(until 17:10)
(206)
|
|
13:45
|
Meta Algorithms Applications in HEP.
(206)
|
|
15:15
|
--- coffee break ---
|
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15:40
|
Hypotheses testing.
(206)
|
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13:45
|
Introductory track
-
Nikita Kazeev
(Yandex School of Data Analysis (RU))
(until 17:10)
(204)
|
|
13:45
|
Ensemble algorithms: random forest, gradient boosting
(204)
|
|
15:15
|
--- coffee break ---
|
|
15:40
|
Hyperparam Optimization
(204)
|
|
17:10
|
--- break ---
|
|
17:30
|
MVA vs cut-and-count techniques in the SM model processes searches and measurements
-
Lesya Shchutska
(University of Florida (US))
(204)
|
|
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12:00
|
--- lunch ---
|
|
13:00
|
Advanced track
-
Tatiana Likhomanenko
(National Research Centre Kurchatov Institute (RU))
(until 16:15)
(206)
|
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13:00
|
Summary: HEP ML analysis sketch, from data to discovery.
(206)
|
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14:30
|
--- coffee break ---
|
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14:45
|
My own first NN using theano.
(206)
|
|
13:00
|
Introductory track
-
Nikita Kazeev
(Yandex School of Data Analysis (RU))
(until 16:15)
(204)
|
|
13:00
|
PCA, RBM and alike on MNIST
(204)
|
|
14:30
|
--- coffee break ---
|
|
14:45
|
Clustering
(204)
|
|
16:15
|
--- break ---
|
|
16:20
|
Multivariate techniques in the Higgs search
-
Josh Bendavid
(CERN)
(204)
|
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18:00
|
--- transfer to the boat ---
|
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19:00
|
--- channel cruise + closing dinner + awards ---
|
|
22:00
|
--- transfer to the hotel ---
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