[ 993MLPETCMU20 ] UE Theoretical Concepts of Machine Learning

Es ist eine neuere Version 2023W dieser LV im Curriculum Master's programme Mechatronics 2023W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M2 - Master's programme 2. year (*)Artificial Intelligence Bernhard Nessler 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2021W
Objectives This practical course complements the lecture "Theoretical Concepts of Machine Learning" and aims at practicing the concepts and methods acquired in the lecture.
  • Generalization error
  • Bias-variance decomposition
  • Error models
  • Model comparisons
  • Estimation theory
  • Statistical learning theory
  • Worst-case and average bounds on the generalization error
  • Structural risk minimization
  • Bayes framework
  • Evidence framework for hyperparameter optimization
  • Optimization techniques
  • Theory of kernel methods
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 2020S known as: INMAWUETCML UE Theoretical Concepts of Machine Learning
Earlier variants They also cover the requirements of the curriculum (from - to)
INMAWUETCML: UE Theoretical Concepts of Machine Learning (2007W-2020S)
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment