After successful completion of the course, students are able to
At the end of the course, the students will be able to read and critique scientific papers. They will be able to analyse the methods proposed in the field of neuro-symbolic AI, and evaluate their strengths and weaknesses. They will be able to prepare an oral presentation of their findings and discuss the studied methods in detail. Finally, they will be able to assess their peers’ work.
The seminar will cover topics belonging to a relevant subfield of machine learning and artificial intelligence (AI): neuro-symbolic AI.
Neuro-symbolic AI is a growing field in knowledge representation and reasoning, machine learning, and AI, whose aim is to augment and combine the strengths of statistical AI with the capabilities of human-like symbolic knowledge and reasoning. Typical problems in neuro-symbolic AI include, e.g., how to integrate background knowledge into machine learning models, how to teach neural models to reason, and how to make machine learning compliant with a set of requirements.
Potential topics for this seminar include:
The students have to
Students attend the kick-off meeting. In the kick-off meeting, each student is allocated to a topic and a paper. After the kick-off meeting, each student is expected to read the assigned paper and the papers belonging to their topic, and to perform a literature review of the related work. Four weeks after the kick-off meeting, each student is expected to schedule a meeting with their advisor to discuss their findings. In June, there will be a presentation day. During the presentation day, each student is expected to prepare a 25 min presentation where their findings are summarised (which is followed by a 5-10 min discussion). After the presentation, students are given feedback on their presentation and seminar paper. Each student will write a seminar paper (6-7 pages + references) on their topic, where the central ideas and methods presented in the talk are summarised.
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