Kritiska perspektiv på datavetenskap och maskininlärning

This course prepares students for critical reflection upon developments in the disciplines of data science and machine learning, within both the commercial and academic spheres. The course can be taken by PhD students with sufficient experience in statistics, data science, and/or machine learning and artificial intelligence.

Upon successful completion of this course, the student will be able to:

• describe and explain problems and pitfalls when interpreting standard experiments performed in these disciplines
• interpret existing work based on fundamental principles (e.g., no free lunch, bias-variance tradeoff, information theory, etc.)
• identify weaknesses and limitations of an existing work, and assess the claims made from the evidence presented
• analyse the reproducibility and replicability of an existing work, and propose improvements
• think broadly about the ethical implications of specific applications of machine learning and data science.

The main content of the course is through the presentation of a series of articles (new and “classic”) that reflect upon research in data science and machine learning, and related disciplines, e.g., applied statistics. Student groups will select and present papers, and help lead discussion about the topic.