Interpretable Machine Learning
We will cover various topics on supervised learning (regression, classification) on tabular data.
- Introduction of various machine learning methods. Topics may include but are not limited to: additive models, tree based methods, neural networks, multivariate adaptive splines
- Discussoion on interpretability. Various topics on interpretable machine learning, including Partial Dependence Plots (PDP), Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) values.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
- Regression & Classification with machine learning methods
- Various machine learning interpretation methods
- Understand the innerworking and limitations of those methods
A general ability to use and the select the right machine learning method to solve practical problems
- Use R relating to the course area
4 hours of lectures and 2 hours of exercises per week for 7 weeks.
A class in regression is very useful. It is possible to follow
the class without these, but of course it will be more demanding.
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Oral examination, 30 min under invigilation
- Type of assessment details
- 30min oral examination with 30min preparation time.
- All aids allowed
Aids are allowed during preperation.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Criteria for exam assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Practical exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 3
- No limit.
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Munir Hiabu (2-726d457266796d33707a336970)
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Courseinformation of students