# Introduction
This project contains material to the university introduction course "Machine Learning for Economics and Finance".
**Git clone**:
`git clone https://gitea.weseng.de/mwio/econometrics-and-machine-learning.git`
# Course content outline
# Section outline
- Supervised Learning: Regressions
- Linear Regression
- Multilinear Regression
- Supervised Learning: Classification
- Logistic Regression
- Cross Validation
- K-fold Cross-validation
- Subset Selection & Shrinkage
- Lasso Regression, Ridge Regression
- Tree-Based Methods
- Classification Trees
- Bagging, Boosting
- Random Forest
- Deep Learnin
- Neural Networks
## Main Reference (ISL):
- James, G., D. Witten, T. Hastie, and R. Tibshirani (2013). An Introduction to Statistical Learning with Applications in Python (Book link).
- The book as well as Python tutorials, datasets and practice exercises are available on the website.
## More advanced (ESL):
- T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning (Book link).