Detailed information |
Original study plan |
Bachelor's programme Artificial Intelligence 2023W |
Objectives |
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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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
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Criteria for evaluation |
Assignments during the semester plus final exam
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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.
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Language |
English |
Study material |
Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
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Changing subject? |
No |
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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