Software Engineering for Smart Data Analytics & Smart Data Analytics for Software Engineering
Dr. Günter Kniesel, Lars Reimann, Firas Kassawat
This lab is part of the Intelligent Systems track of the M.Sc. curriculum.
The lab will consist of two tracks, each with topics for groups of 2 to 4 students.
Track 1 will be dedicated to the use of semantic data web technologies for representing semantic information about software systems. In particular, we will extract information from the code and the documentation of a widely-used machine learning library, scikit-learn, and represent this information as a knowledge graph (KG), formed by RDF triples RDF triples. The KG will later be used for a variety of tasks that could be topics for master theses.
The work in this track will encompass creation of a KG that describes the scikit-learn library:
Track 2 will be dedicated to “Explainability”. A machine learning algorithm is said to be explainable, if the reasons why its results make sense can be understood by humans. Explainablility is an active area of research because non-explainability of results is one of the main factors hampering wider-spread adoption of ML, especially for critical tasks. Some explainability approaches already exist, but only for numerical data. In the lab we will try to generalize some of them to the domain of the semantic web, that is, RDF knowledge graphs. In particular, possible groups could work on
Interested in further details? Then see the registration details for the info meeting below.
Lab participants must be familiar with
If the number of interested students exceeds the number of available seats, priority will be given to those who have additional knowledge in at least one of the following:
The final deliverables (code, documentation, manual) will be peer-reviewed.
Interested students are required to send an e-mail to all organizers mentioned at the bottom of the page. In this mail you should state
If we think that you have sufficient background we will send you an invitation to the info meeting. The info meeting will introduce the above mentioned topics in more detail, and explain the requirements for the lab, the infrastructure, schedule, etc.. You will then be able to choose a topic.
We will form groups for each topic based on our assessment of your skills. If in doubt, we will give you a small task that requires the respective skills and assess how well you can solve it.
The info meeting will take place on Friday, 15.10.2021, 9:00 (st) in room 1.047. Depending on the development of the COVID-19 pandemic, the place could change. In the worst case, the info meeting will be online.
The following times are fixed:
Depending on the development of the COVID-19 pandemic, the places could change. In the worst case, the lab will be partly or fully online.
mdse course lists iai uni bonn de← fill spaces with “
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|Dr. Günter Kniesel||gk cs uni bonn de||(0228) 73-4511||1.066|
|Lars Reimann||reimann cs uni bonn de||(0228) 73-4…||1.065|
|Firas Kassawat||kassawat cs uni bonn de||(0228) 73-4…||1.065|
← fill spaces with “
@ . . - .”)