Machine Learning (Master level course)
Since 2016, I teach the Machine Learning course at NTNU. In this master-level course, we cover machine learning foundations and core machine learning methods.
The course gives an introduction to the principles and methods for automatic learning in computer systems. Classical syntax-based learning methods, as well as more knowledge-intensive methods, are described. The main empahsis is on symbolic methods, where explicit concepts and relationships are learned. Statistical generalizations, ensemble methods, and deep learning are also included. The strengths and weaknesses of various methods are discussed.
Learning methods in case-based reasoning is integrated with problem-solving within the CBR cycle. Numerical and cognitive models for similarity assessment will be discussed, together with different learning system architectures. Methods that combine case-based and generalisation-based inferences will be discussed as well.
Link to the course: https://www.ntnu.edu/studies/courses/TDT4173
Knowledge-intensive CBR (Master level course)
This course is a theory module for final year master’s students at NTNU. A selected set of papers (including book chapters) related to case-based reasoning combined with general domain knowledge and other method components will be discussed. The actual focus to some extent depends on the interests of the students taking the course. The course will be run as a set of seminar meetings in which selected papers are summarized by the students and discussed in the group.
Link to the course page: https://research.idi.ntnu.no/cbr/tdt55/
Advanced Topics in CBR (PhD Course)
Between 2016-2020 I taught the PhD course on advanced topics of CBR. The course was two-fold: The theory part discussed methods for similarity assessment, case adaptation, case learning, and case base maintenance. It includes methods that combine CBR with other reasoning forms, also triggered by the recent BigData focus. Methods that reason from past concrete situations (case-based) is the primary target in the course, but methods that reason from generalized models (model-based) will also be characterized. Integrated reasoning methods that combine the two, and address problem solving as well as machine learning targets, will be discussed and related to developments in the above method areas. The specific set of topics covered will to some extent depend on the interests of the students taking the course. The second part focuses on developing a CBR application using the students or an open dataset. Here we use the open-source tool myCBR and our further developments.
Link to the course: https://www.ntnu.edu/studies/courses/IT8000
Other teaching activities
I am or have been involved in a number of courses at NTNU: