Interpretable Machine Learning
In this course, you will learn about various regression and classification methods, various post-hoc interpretation methods, and understand the inner working and limitations of those methods.
Course description
Content
We will cover various topics on supervised learning (regression, classification) on tabular data:
- Fundamentals of statistical learning
- Linear models with and without penalization
- Course of dimensionality in nonparametric models
- Additive models
- Tree based methods and neural networks
- Post-hoc interpretability
Recommended academic qualifications
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.
Place
The University of Copenhagen
- Department of Mathematical Sciences
Contact
Continuing and Lifelong Learning, UCPH Education
Write to lifelonglearning@adm.ku.dk