Inhalt

[ 536MLPEMSTU19 ] UE Machine Learning: Supervised Techniques

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS B2 - Bachelor's programme 2. year (*)Artificial Intelligence Arturs Berzins 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2025W
Learning Outcomes
Competences
See the corresponding lecture.
Skills Knowledge
  • Implementing Classification and Regression Models (k3)

Students can practically implement classification and regression models using various supervised learning techniques, understanding how to fit models to data and make predictions.

  • Evaluating Model Performance Using Metrics (k4)

Students are able to compute and interpret performance metrics, such as confusion matrices and ROC curves, to assess the quality and reliability of machine learning models.

  • Applying Cross-Validation and Hyperparameter Tuning (k4)

Students can perform cross-validation and optimize hyperparameters to enhance model robustness and prevent overfitting, ensuring better generalization on new data.

  • Experimenting with Advanced Supervised Learning Methods (k4)

Students are capable of applying algorithms like logistic regression, support vector machines (SVMs), and neural networks, exploring their performance and limitations in different problem contexts.

  • Analyzing Time Series Data and Sequence Prediction (k4)

Students can handle sequence data and apply machine learning techniques to analyze and predict time series patterns, understanding their structure and temporal dependencies.

  • Implementing Ensemble Methods and Feature Engineering (k4)

Students can use ensemble methods such as bagging and boosting to improve model performance and apply feature selection and construction techniques to refine input data for models.

  • Balancing Bias and Variance in Model Training (k5)

Students are able to identify and address issues of under- and overfitting, effectively balancing bias and variance to optimize model performance.

Students have practical knowledge of implementing and evaluating supervised learning models, including concepts of classification, regression, and model validation. They know how to apply advanced techniques like SVMs, neural networks, ensemble methods, and feature engineering while understanding how to address model complexity and performance challenges through cross-validation and hyperparameter tuning.
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
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)
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment