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2022 CAS AML Projects

Upload platform for final CAS AML projects


The CAS Project yields 4 ECTS points (~120 hours workload) and is a machine learning project on data of your choice. The deliverable is a report with supporting notebooks. The project should have its github respository. You can and are encouraged to work in teams. The project report is typically between 10 and 20 pages. Your work can be a consolidation of things you have done during the CAS or a new project on a new dataset.

2023-05-10 If you need consulting for your project, book a slot here (of course you can also try to reach us by email). Zoom room (you may also come to the office 227 in Sidlerstrasse 5)
2023-06-15 Deadline for creating a link (here on Ilias) to the GitHub repository with report and notebooks
2023-06-21 Voluntary presentation day. If you want to present, book your slot here.

Project Report Outline (this is a guideline, you may do it a bit differently):
(if you are writing a publication based on your project, you can of course use the publication draft as report)
- Front page with title, author names and emails, confidential statement if needed, abstract
- Introduction
- Data (possibly with data flow, data quality, feature engineering, preprocessing, cleaning, ...)
- Exploratory data analysis (with descriptive statistics and plots)
- Machine learning analysis
- Results Discussion (discuss significance of and uncertainty on the results)
- Conlcusion and Outlook
- Acknowledgements
- References

Formal criteria for the report:
- Is the report acceptable regarding grammar and syntax?
- Is the report sufficiently organised (title, author, affiliation, contact information, references) ?
- Are illustrations, tables and numerical presentations acceptable (visually, axis labelling, referenced in text) ?
- Does the report/poster reference data sources and previous works sufficiently ?
- Does the report apply terminology, methods and best practices taught in the CAS ?
- Is the data science task well-defined and clearly formulated ?
- Is the data set, the metadata and the data quality sufficiently described ?
- Are the applied methods sufficiently described ? 
- Are the analysis results critically assessed with uncertainty estimations ?