Syllabus, Master's level, 2ST129
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Statistics A1F
- Grading system
- Fail (U), Pass (G), Pass with distinction (VG)
- Finalised by
- The Department Board, 14 October 2022
- Responsible department
- Department of Statistics
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:
- have a good knowledge of a large number of machine learning models
- be able to use methods for evaluating and improving predictive models
- be able to describe and discuss ethical aspects of big data and black box-models
- be able to handle big data
- be able to train and use machine learning models in R
- be able to train and use neural networks using Keras/Tensorflow
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.
No reading list found.