| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*) This practical course complements the lecture "Machine Learning: Supervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
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| Lehrinhalte |
(*)- Basics of classification and regression
- Evaluation of machine learning results (confusion matrices, ROC)
- Under- and overfitting / bias and variance
- Cross-validation and hyperparameter selection
- Logistic regression
- Support vector machines and kernels
- Neural networks and deep networks
- Time series (sequence) analysis
- Bagging and boosting
- Feature selection and feature construction
|
| 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 |
(*)875BIMLMSTU16: UE Machine Learning: Supervised Techniques (1,5 ECTS)
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
875BIMLMSTU16: UE Machine Learning: Supervised Techniques (2016W-2019S)
675MLDAMSTU13: UE Machine Learning: Supervised Techniques (2013W-2016S)
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