41 lines
1.5 KiB
Markdown
41 lines
1.5 KiB
Markdown
# Introduction
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This project contains material to the university introduction course "Machine Learning for Economics and Finance".
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**Git clone**:<br>
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`git clone https://gitea.weseng.de/mwio/econometrics-and-machine-learning.git`
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# Course content outline
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# Section outline
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<!--**...**:<br> -->
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- Supervised Learning: Regressions
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- Linear Regression
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- Multilinear Regression
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- Supervised Learning: Classification
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- Logistic Regression
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- Cross Validation
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- K-fold Cross-validation
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- Subset Selection & Shrinkage
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- Lasso Regression, Ridge Regression
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- Tree-Based Methods
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- Classification Trees
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- Bagging, Boosting
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- Random Forest
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- Deep Learnin
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- Neural Networks
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## Main Reference (ISL):
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- 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>.
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- The book as well as Python tutorials, datasets and practice exercises are available on the website.
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## More advanced (ESL):
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- 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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<!-- <a href="https://www.raspberrypi.org/documentation/installation/installing-images/README.md" target="_blank">Introduction: Installing operating system images on the Raspberry Pi</a> -->
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