Conveners
Section 1. Introduction into ML: Data handling in Python
- Artem Maevskiy (National Research University Higher School of Economics (RU))
Section 1. Introduction into ML: Ensembles-1
- Nikita Kazeev (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: Quality Metrics-2
- 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: Useful hacks.
- Mikhail Hushchyn (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: Quality Metrics-1
- Mikhail Hushchyn (Yandex School of Data Analysis (RU))
Section 1. Introduction into ML: Logistic regression-2
- 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: Linear Regression
- Artem Maevskiy (National Research University Higher School of Economics (RU))
Section 1. Introduction into ML: Clustering-2
- Mikhail Hushchyn (Yandex School of Data Analysis (RU))
Mikhail Hushchyn
(Yandex School of Data Analysis (RU))
Clustering. K-Means. Quality metrics for clustering
Mr
Nikita Kazeev
(Yandex School of Data Analysis (RU))
Splitting rule. Classification and regression decision trees
Nikita Kazeev
(Yandex School of Data Analysis (RU))
Bagging and Random Forest. Stacking and blending.
Artem Maevskiy
(National Research University Higher School of Economics (RU))
Linear regression. Analytical solution. Gradient descent. Numerical solution. Polynomial features.
Artem Maevskiy
(National Research University Higher School of Economics (RU))
Artem Maevskiy
(National Research University Higher School of Economics (RU))
Linear models regularization. Probabilistic interpretation of linear models (regression and classification).
Mikhail Hushchyn
(Yandex School of Data Analysis (RU))
Quality metrics for classification and regression
Mikhail Hushchyn
(Yandex School of Data Analysis (RU))
How to test your model. Cross validation. Statistical model comparison
Artem Maevskiy
(National Research University Higher School of Economics (RU))
Mikhail Hushchyn
(Yandex School of Data Analysis (RU))
Feature engineering, importance and selection.