As the development of machine learning algorithms advances rapidly, they play larger and larger roles for our societies. People both in- and outside the technical domain are rightfully concerned about the fairness of such algorithms:How can we explain, justify and assign accountability for automated decisions made by complex ML models? How do deep learning algorithms propagate existing bias and stereotypes? How can we define and measure algorithmic fairness?While those questions certainly aren't engineering problems alone, this year's summer school will consider them from a technical perspective.
The course consists of five days (Monday-Friday) of lectures and exercises. The lectures will cover theoretical aspects such as explainability of deep learning models, causal reasoning and fairness metrics. Technical aspects, programming and application of the developed concepts will be explored in tutorials and exercises. The course (2.5 ECTS points) is passed by handing in a small report on one of the topics covered in the course. For further course details click here.
Technical University of Denmark, building 324, room 060.
Every day August August 26-30, 2019, exact starting and end times TBA.
General understanding of machine learning, statistical modeling, mathematics and computer science. Programming experience, ideally in Python. For the course you are required to bring your own laptop computer with a Python installation.
Registration is closed. Accepted participants have been notified. If you have any questions regarding the registration please email Wanja Andersen email@example.com
For academics (masters and PhD students) there is no registration fee for the course. For all other participants a course fee of DKK 8250 will be charged. Participants are to cover all other costs such as food, accommodation, and travel expenses.
For practical information regarding transportation and accommodation click here.
2018 version of 02901 Advanced Topics in Machine Learning
2017 version of 02901 Advanced Topics in Machine Learning
2016 version of 02901 Advanced Topics in Machine Learning
2015 version of 02901 Advanced Topics in Machine Learning
2014 version of 02901 Advanced Topics in Machine Learning
For further information, please contact: