||Bachelorstudium Bioinformatics 2013W
||(*) This practical course complements the lecture "Machine Learning: Supervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
- 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
||(*)Marking is based on homework
||(*)Students are given assignments in 1-2 week intervals. Homework must be handed in. Results are to be presented and discussed in the course.
||(*)Assignments and homework submissions are managed via JKU Moodle.
Where necessary, complimentary course material is provided for download.
||(*)in collaboration with 875BIN2MUTU13: UE Machine Learning: Unsupervised Techniques (1,5 ECTS) equivalent to
875BIN2TMLU12: UE Theoretical Bioinformatics and Machine Learning (3 ECTS) -or- BIMPHUEBIN2: UE Bioinformatik II: Theoretische Bioinformatik und Maschinelles Lernen (3 ECTS)