Sixth Machine Learning in High Energy Physics Summer School 2020

Name: Sixth Machine Learning in High Energy Physics Summer School 2020
Start: 2020-07-16T09:00:00+02:00
End: 2020-07-30T20:30:00+02:00
Location: Zoom

16–30 Jul 2020

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Europe/Berlin timezone

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mlhep2020@yandex.ru

Session

Section 1. Introduction into ML

16 Jul 2020, 11:30

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Section 1. Introduction into ML: Basics

Artem Maevskiy (National Research University Higher School of Economics (RU))

Section 1. Introduction into ML: Linear Regression

Artem Maevskiy (National Research University Higher School of Economics (RU))

Section 1. Introduction into ML: Logistic regression-1

Artem Maevskiy (National Research University Higher School of Economics (RU))

Section 1. Introduction into ML: Logistic regression-2

Artem Maevskiy (National Research University Higher School of Economics (RU))

Section 1. Introduction into ML: Quality Metrics-1

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

Section 1. Introduction into ML: Quality Metrics-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

Section 1. Introduction into ML: Decision trees-1

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

Section 1. Introduction into ML: Ensembles-1

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

Section 1. Introduction into ML: Ensembles-2

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

Section 1. Introduction into ML: Useful hacks.

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

Section 1. Introduction into ML: Clustering-1

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

Section 1. Introduction into ML: Clustering-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

There are no materials yet.

99. Introduction into supervised learning

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 11:30

Practical session
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113. Seminar: Data Handling in Python

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 12:00
100. Linear Regression

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 13:30

Linear regression. Analytical solution. Gradient descent. Numerical solution. Polynomial features.
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114. Seminar

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 14:00
101. Logistic regression-1

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 15:00
115. Seminar

16/07/2020, 15:30
102. Logistic regression-2

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 16:30

Linear models regularization. Probabilistic interpretation of linear models (regression and classification).
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116. Seminar

Artem Maevskiy (National Research University Higher School of Economics (RU))

16/07/2020, 17:00
103. Quality Metrics-1

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

17/07/2020, 09:00

Quality metrics for classification and regression
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118. Seminar: Quality Metrics-1

17/07/2020, 09:30
104. Quality Metrics-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

17/07/2020, 10:30

How to test your model. Cross validation. Statistical model comparison
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119. Seminar: Quality Metrics-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

17/07/2020, 11:00
105. Decision trees-1

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

17/07/2020, 12:00

Splitting rule. Classification and regression decision trees
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120. Seminar: Decision trees-1

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

17/07/2020, 12:30
107. Ensembles-1

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

17/07/2020, 14:00

Bagging and Random Forest. Stacking and blending.
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122. Seminar: Ensembles-1

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

17/07/2020, 14:30
112. Ensembles-2

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

17/07/2020, 15:30

Gradient boosting.
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123. Seminar: Ensembles-2

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

17/07/2020, 16:00
109. Useful hacks

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 09:00

Feature engineering, importance and selection.
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124. Seminar: Useful hacks.

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 09:30
110. Clustering-1

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 10:30

Clustering. K-Means. Quality metrics for clustering
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125. Seminar: Clustering-1

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 11:00
111. Clustering-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 12:00

Hierarchical clustering and DBSCAN.
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126. Seminar: Clustering-2

Mikhail Hushchyn (Yandex School of Data Analysis (RU))

18/07/2020, 12:30
106. Decision trees-2

Nikita Kazeev (Yandex School of Data Analysis (RU))
121. Seminar: Decision trees-2

Building timetable...

Sixth Machine Learning in High Energy Physics Summer School 2020

Support

Session

Section 1. Introduction into ML

Zoom

Conveners

Section 1. Introduction into ML: Basics

Section 1. Introduction into ML: Linear Regression

Section 1. Introduction into ML: Logistic regression-1

Section 1. Introduction into ML: Logistic regression-2

Section 1. Introduction into ML: Quality Metrics-1

Section 1. Introduction into ML: Quality Metrics-2

Section 1. Introduction into ML: Decision trees-1

Section 1. Introduction into ML: Ensembles-1

Section 1. Introduction into ML: Ensembles-2

Section 1. Introduction into ML: Useful hacks.

Section 1. Introduction into ML: Clustering-1

Section 1. Introduction into ML: Clustering-2

Presentation materials

Choose timezone

Sixth Machine Learning in High Energy Physics Summer School 2020

Support

Conveners

Section 1. Introduction into ML: Basics

Section 1. Introduction into ML: Linear Regression

Section 1. Introduction into ML: Logistic regression-1

Section 1. Introduction into ML: Logistic regression-2

Section 1. Introduction into ML: Quality Metrics-1

Section 1. Introduction into ML: Quality Metrics-2

Section 1. Introduction into ML: Decision trees-1

Section 1. Introduction into ML: Ensembles-1

Section 1. Introduction into ML: Ensembles-2

Section 1. Introduction into ML: Useful hacks.

Section 1. Introduction into ML: Clustering-1

Section 1. Introduction into ML: Clustering-2

Presentation materials