This repository contains hands-on instructions for the courses Machine Learning and Statistical Inference, in which I was a teaching assistant during the fall of 2023.
In the Machine Learning hands-on, we discuss different ensemble learning algorithms such as bagging, boosting, and stacking. We also get familiar with popular models in each class and learn their API in Scikit-learn. Finally, we evaluate their performance.
In the Statistical Learning hands-on, we have three different sessions. The first one is about contingency tables and their use. The second one is about non-parametric tests, and the third one is about the Kolmogorov-Smirnov test. We get familiar with these concepts, their applications, and how to perform them using their API in Python.
To use this repository, simply clone it to your local machine and navigate to the corresponding directory for each hands-on session.
This repository is licensed under the MIT License. See the LICENSE file for details.
If you have any questions or feedback, feel free to contact me at siavashrazmi74@gmail.com. I'll be happy to hear from you!