Data Analysis Block Course: Statistics and Machine learning
from
Monday, 21 September 2026 (09:00)
to
Friday, 2 October 2026 (20:00)
Monday, 21 September 2026
09:00
Lecture - Introduction
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Introduction
Michael Schmelling
(
Max Planck Society (DE)
)
09:00 - 10:30
10:45
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
10:45 - 12:00
13:30
Lecture - Error propagation
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Error propagation
Michael Schmelling
(
Max Planck Society (DE)
)
13:30 - 14:30
14:45
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
14:45 - 16:00
Tuesday, 22 September 2026
09:00
Lecture - Information, inference and confidence level intervals
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Information, inference and confidence level intervals
Michael Schmelling
(
Max Planck Society (DE)
)
09:00 - 10:30
10:45
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
10:45 - 12:00
13:00
Lecture - Least Squares Fitting
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Least Squares Fitting
Michael Schmelling
(
Max Planck Society (DE)
)
13:00 - 14:00
14:15
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
14:15 - 15:30
Wednesday, 23 September 2026
09:00
Lecture - Maximum Likelihood, Sandwich estimator, Bootstrap
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Maximum Likelihood, Sandwich estimator, Bootstrap
Michael Schmelling
(
Max Planck Society (DE)
)
09:00 - 10:30
10:45
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
10:45 - 12:00
13:00
Lecture - Markov Chain Monte Carlo, COWs
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Markov Chain Monte Carlo, COWs
Michael Schmelling
(
Max Planck Society (DE)
)
13:00 - 14:00
14:15
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
14:15 - 15:30
Thursday, 24 September 2026
09:00
Lecture - sPlot, sFit, Unfolding
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - sPlot, sFit, Unfolding
Michael Schmelling
(
Max Planck Society (DE)
)
09:00 - 10:30
10:45
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
10:45 - 12:00
13:00
Lecture - Unfolding, Harmonic analysis
-
Michael Schmelling
(
Max Planck Society (DE)
)
Lecture - Unfolding, Harmonic analysis
Michael Schmelling
(
Max Planck Society (DE)
)
13:00 - 14:00
14:15
Exercises
-
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
Exercises
Jan Peter Wagner
(
Technische Universitaet Dortmund (DE)
)
14:15 - 15:30
Friday, 25 September 2026
09:00
Automatic Differentiation
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Automatic Differentiation
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
09:00 - 10:30
10:45
Exercises - Automatic Differentiation
Exercises - Automatic Differentiation
10:45 - 12:15
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/1_automatic_differentiation.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/1_automatic_differentiation.jl)
13:30
Machine Learning (loss, ROC)
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Machine Learning (loss, ROC)
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
13:30 - 15:00
- Slides - lecture-1B-entropy.jl: introduction to entropy - lecture-1B-mse.jl: MSE as the simplest loss function
15:15
Exercises - Samples, FoM, ROC
Exercises - Samples, FoM, ROC
15:15 - 16:45
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/2_samples_fom_roc.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/2_samples_fom_roc.jl)
Saturday, 26 September 2026
Sunday, 27 September 2026
Monday, 28 September 2026
09:00
Decision Trees, Adaboost
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Decision Trees, Adaboost
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
09:00 - 10:30
- Slides - lecture-2A-split.jl: splitting index demonstration - lecture-2A-trees.jl: decision tree demonstration
10:45
Exercises - Decision Trees
Exercises - Decision Trees
10:45 - 12:15
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/3_decision_trees.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/3_decision_trees.jl)
13:30
Decision Trees, GradientBoost
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Decision Trees, GradientBoost
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
13:30 - 15:00
- Slides - [GBDT playground](https://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html)
15:15
Exercises - AdaBoost, GradientBoost
Exercises - AdaBoost, GradientBoost
15:15 - 16:45
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/4_boosting.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/4_boosting.jl)
Tuesday, 29 September 2026
09:00
Neural Networks : General introduction
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Neural Networks : General introduction
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
09:00 - 10:30
- Slides - lecture-3A-xor.jl: learning xor demonstrator - gradient descent app: github.com/lilipads/gradient_descent_viz
10:45
Exercises - Neural Networks
Exercises - Neural Networks
10:45 - 12:15
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/5_neural_networks.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/5_neural_networks.jl)
13:30
Neural networks : Bias-Variance TradeOff and regularization techniques
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Neural networks : Bias-Variance TradeOff and regularization techniques
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
13:30 - 15:00
- Slides: Lecture 3 pdf contains but A,B parts - lecture-3B-UAT.jl: Universal approximation theory demonstration - lecture-3B-NMIST.jl: training of MNIST classification - playground.tensorflow.org
15:15
Exercises - NNs on example data
Exercises - NNs on example data
15:15 - 16:45
[Exercise notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/6_neural_networks.jl) [Solution notebook](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/solutions/6_neural_networks.jl)
Wednesday, 30 September 2026
09:00
Generative networks, Normalizing flow
-
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
Generative networks, Normalizing flow
Mikhail Mikhasenko
(
Ruhr Univeristy Bochum
)
09:00 - 11:00
- Slides - lecture-4A-transformations.jl: demonstration of bijectors - lecture-4A-RQS.jl: tuning RQS
11:15
Exercises - Normalizing flow
Exercises - Normalizing flow
11:15 - 12:30
[Notebook NF basics](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/7_normalizing_flow_basics.jl) [Notebook NF training](https://github.com/RUB-EP1/Data-Analysis-Block-Course-2025/blob/main/exercises/8_normalizing_flow.jl)
16:30
BBQ
BBQ
16:30 - 19:35
Sign up here: https://hackmd.e5.physik.tu-dortmund.de/3XzYG8C4ScuKaNh43QB3GQ?both Location: https://maps.app.goo.gl/qwZvu8eUB14JzKTw5
Thursday, 1 October 2026
Friday, 2 October 2026
09:00
Written Exam -- date to be fixed
Written Exam -- date to be fixed
09:00 - 10:30
- Pen and paper, calculator (non-programmable) - Time: 1:30h - 5-6 Problems - ChatGPT is not allowed