Interpretable Machine Learning
Course content
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
Knowledge:
- Regression & Classification with machine learning methods
- Various machine learning interpretation methods
- Understand the innerworking and limitations of those methods
Skills:
A general ability to use and the select the right machine learning method to solve practical problems
Competences:
- Use R relating to the course area
4 hours of lectures and 2 hours of exercises per week for 7 weeks.
Lecture notes
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.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 30 min under invigilation
- Type of assessment details
- 30min oral examination with 30min preparation time.
- Exam registration requirements
-
Two mandatory assignments must be approved and valid before the student is allowed attending the exam.
- Aid
- All aids allowed
Aids are allowed during preperation.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Internal examiners
- Re-exam
-
Same as ordinary exam.
If the the two mandatory homework assignments were not approved before the ordinary exam they must be resubmitted. They must be handed in three weeks before the re-exam and must be approved before the commencement of the re-exam.
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)
- Category
- Hours
- Lectures
- 28
- Preparation
- 110
- Practical exercises
- 14
- Project work
- 42
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK23006U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 3
- Schedulegroup
-
B
- Capacity
- No limit.
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Munir Eberhardt Hiabu (2-726d457266796d33707a336970)
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende