Lunch Lecture: Computer Science

This quarter’s edition will be given by Christoph Lofi!

Abstract of the topic:
While there is a lot of public focus on the development of fancy machine learning algorithms, AI systems typically require large high-quality data sets for training, validation, and testing. However, such data sets are hard to come by, and are often fragmented, biased, incomplete, low quality, unfit for the task, or even simply wrong. Many issues with modern machine learning systems can be traced back to issues in the underlying data. In this lecture, there will be a quick overview of different Data Engineering challenges and top-level methods which try to compensate for these problems.

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