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:
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.
Read more about cookies.