The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7,5 credits programming in R, Python or Julia.
After completing the course, the student is expected to:
Regularised regression, nearest neighbour methods, decision trees, ensemble models, bagging, out-of-sample evaluations, handling of big data, ethical questions regarding big data and predictive models, methods for explainable machine learning, and neural networks: architectures, gradient descent, generative models, regularisation and adversarial examples.
Instruction is given in the form of lectures, labs and/or as seminars.
The examination takes place through written and/or oral presentation of compulsory assignments.
This course is part of the master degree program in statistics.
The reading list is missing. For further information, please contact the responsible department.
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