# Introduction
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 https://gitea.weseng.de/mwio/econometrics-and-machine-learning.git`

# Course content outline
![File content](ml_courseplan_python_2024.svg)

# Section outline
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- Introduction to Python
- Excercises with Python
- Supervised Learning: Regressions
  - Linear Regression
  - Multilinear Regression
- Supervised Learning: Classification
  - Logistic Regression
- Cross Validation
  - K-fold Cross-validation
- *Problem Set 1*
- Subset Selection & Shrinkage
  - Lasso Regression, Ridge Regression
- Tree-Based Methods
  - Classification Trees
  - Bagging, Boosting
  - Random Forest
- *Problem Set 2*
- Deep Learnin
  - Neural Networks


## Main Reference (ISL):
- James, G., D. Witten, T. Hastie, and R. Tibshirani (2013). <a href="https://statlearning.com/" target="_blank">An Introduction to Statistical Learning with Applications in Python (Book link)</a>.
- 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. <a href="https://web.stanford.edu/~hastie/ElemStatLearn/" target="_blank">The Elements of Statistical Learning (Book link)</a>.


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