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 Computer Science Sepp Hochreiter 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2019W
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 Marking is based on homework
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
Corresponding lecture 875BIMLMSTU16: UE Machine Learning: Supervised Techniques (1,5 ECTS)
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment