| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2020W |
| Objectives |
This practical course complements the lecture "Theoretical Concepts of Machine Learning" and aims at practicing the concepts and methods acquired in the lecture.
|
| Subject |
- Generalization error
- Bias-variance decomposition
- Error models
- Model comparisons
- Estimation theory
- Statistical learning theory
- Worst-case and average bounds on the generalization error
- Structural risk minimization
- Bayes framework
- Evidence framework for hyperparameter optimization
- Optimization techniques
- Theory of kernel methods
|
| Criteria for evaluation |
Marking is based on homework
|
| Methods |
Students are given assignments in 1-2 week intervals. Homework must be handed in. Results are to be presented and discussed in the course.
|
| Language |
English |
| Study material |
Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
|
| Changing subject? |
No |
| Corresponding lecture |
INMAWUETCML: UE Theoretical Concepts of Machine Learning (1.5 ECTS)
|
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
INMAWUETCML: UE Theoretical Concepts of Machine Learning (2007W-2020S)
|
