We want you to answer the three different projects by handing in reports written like a standard scientific/technical report. The links at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects Furthermore, at the same link, you can find examples of previous reports. How to write reports will also be discussed during the various lab sessions. Please do ask us if you are in doubt.
When using codes and material from other sources, you should refer to these in the bibliography of your report, indicating wherefrom you for example got the code, whether this is from the lecture notes, softwares like Scikit-Learn, TensorFlow, PyTorch or other sources such AI software. These should always be cited correctly. How to cite some of the libraries is often indicated from their corresponding GitHub sites or websites, see for example how to cite Scikit-Learn at https://scikit-learn.org/dev/about.html.
We enocurage you to use tools like ChatGPT or similar in writing the report. If you use for example ChatGPT, please do cite it properly and include (if possible) your questions and answers as an addition to the report. This can be uploaded to for example your website, GitHub/GitLab or similar as supplemental material.
If you would like to study other data sets, feel free to propose other sets. What we have proposed here are mere suggestions from our side. If you opt for another data set, consider using a set which has been studied in the scientific literature. This makes it easier for you to compare and analyze your results. Comparing with existing results from the scientific literature is also an essential element of the scientific discussion. The University of California at Irvine with its Machine Learning repository at https://archive.ics.uci.edu/ml/index.php is an excellent site to look up for examples and inspiration. Kaggle.com is an equally interesting site. Feel free to explore these sites. When selecting other data sets, make sure these are sets used for regression problems (not classification).