Lecture/Exercises - Basics

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Basics

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Basics

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Error propagation

• Lorenzo Nisi (Technische Universitaet Dortmund (DE))
• Michael Schmelling (Max Planck Society (DE))

Lecture/Exercises - Information, inference and confidence level intervals

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Information, inference and confidence level intervals

• Lorenzo Nisi (Technische Universitaet Dortmund (DE))
• Michael Schmelling (Max Planck Society (DE))

Lecture/Exercises - Least Squares

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Maximum Likelihood

• Lorenzo Nisi (Technische Universitaet Dortmund (DE))
• Michael Schmelling (Max Planck Society (DE))

Lecture/Exercises - Sandwich estimator and bootstrap

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - COWs and sPlot

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Markov Chain Monte Carlo

• Michael Schmelling (Max Planck Society (DE))
• Lorenzo Nisi (Technische Universitaet Dortmund (DE))

Lecture/Exercises - Unfolding

• Lorenzo Nisi (Technische Universitaet Dortmund (DE))
• Michael Schmelling (Max Planck Society (DE))

Automatic Differentiation

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

Exercises - Automatic Differentiation

• Hendric Jonas (Ruhr-Universitaet Bochum (DE))

Machine Learning (loss, ROC)

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

Exercises - Samples, FoM, ROC

• Christoph Tönnis (Ruhr-Universitaet Bochum (DE))

Decision Trees, Adaboost

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

- Slides - lecture-2A-split.jl: splitting index demonstration - lecture-2A-trees.jl: decision tree demonstration

Exercises - Decision Trees

• Hendric Jonas (Ruhr-Universitaet Bochum (DE))

Decision Trees, GradientBoost

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

- Slides - [GBDT playground](https://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html)

Exercises - AdaBoost, GradientBoost

• Christoph Tönnis (Ruhr-Universitaet Bochum (DE))

Neural Networks : General introduction

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

- Slides - playground.tensorflow.org - gradient descent app: github.com/lilipads/gradient_descent_viz

Exercises - Neural Networks

• Hendric Jonas (Ruhr-Universitaet Bochum (DE))

Convolutional Neural Networks

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

- Slides - Exploring AlexNet

Exercises - NNs on example data

• Christoph Tönnis (Ruhr-Universitaet Bochum (DE))

Transformers

• Mikhail Mikhasenko (Ruhr Univeristy Bochum)

- Slides

Exercises - Transformers

• Marian Stahl (Ruhr-Universitaet Bochum (DE))

Agentic coding