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Updated the README.md

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Marcel Weschke
2025-12-03 12:37:04 +01:00
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Machine Learning for Economics and Finance/README.md
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# Introduction # Introduction
This project contains material to the university introduction course "Machine Learning for Economics and Finance". This repository contains materials designed to complement the university course "Machine Learning for Economics and Finance." The content is based on the foundational work by James, G., Witten, D., Hastie, T., and Tibshirani, R. in their book "An Introduction to Statistical Learning."
These materials have been adapted by Prof. Dr. Ole Wilms to fit the course requirements and further transformed and expanded by me from R to Python. I have also introduced additional topics, including an introduction to Python, optimal data handling techniques—from loading and cleaning datasets to final evaluations—ensuring students are well-equipped for practical applications in future semesters.
**Git clone**:<br> **Git clone**:<br>
`git clone https://gitea.weseng.de/mwio/econometrics-and-machine-learning.git` `git clone https://gitea.weseng.de/mwio/econometrics-and-machine-learning.git`
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- Logistic Regression - Logistic Regression
- Cross Validation - Cross Validation
- K-fold Cross-validation - K-fold Cross-validation
- *Problem Set 1*
- Subset Selection & Shrinkage - Subset Selection & Shrinkage
- Lasso Regression, Ridge Regression - Lasso Regression, Ridge Regression
- Tree-Based Methods - Tree-Based Methods
- Classification Trees - Classification Trees
- Bagging, Boosting - Bagging, Boosting
- Random Forest - Random Forest
- *Problem Set 2*
- Deep Learnin - Deep Learnin
- Neural Networks - Neural Networks