| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2021W |
| Objectives |
This practical course complements the lecture "Machine Learning: Supervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
|
| Subject |
- 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
|
| Criteria for evaluation |
Assignments during the semester plus final exam
|
| 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 |
| Further information |
Until term 2019S known as: 875BIMLMSTU16 UE Machine Learning: Supervised Techniques
until term 2016S known as: 675MLDAMSTU13 UE Machine Learning: Supervised Techniques
|
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
875BIMLMSTU16: UE Machine Learning: Supervised Techniques (2016W-2019S)
675MLDAMSTU13: UE Machine Learning: Supervised Techniques (2013W-2016S)
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