| Detailinformationen |
| Quellcurriculum |
Masterstudium Artificial Intelligence 2020W |
| Ziele |
(*)This practical course complements the lecture "Theoretical Concepts of Machine Learning" and aims at practicing the concepts and methods acquired in the lecture.
|
| Lehrinhalte |
(*)- 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
|
| Beurteilungskriterien |
(*)Marking is based on homework
|
| Lehrmethoden |
(*)Students are given assignments in 1-2 week intervals. Homework must be handed in. Results are to be presented and discussed in the course.
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
|
| Lehrinhalte wechselnd? |
Nein |
| Äquivalenzen |
(*)INMAWUETCML: UE Theoretical Concepts of Machine Learning (1.5 ECTS)
|
| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
INMAWUETCML: UE Theoretical Concepts of Machine Learning (2007W-2020S)
|
