Table of contents

The course is connected to the following study programs

Teaching language

English

Learning outcomes

After completion of the course, the students should be able to

  • Implement on-line learning algorithms, including bandit algorithms, learning automata, Tsetlin machines, Thompson sampling, and various kinds of Bayesian learning.
  • Analyse and evaluate learning systems empirically.
  • Design and implement decentralized learning systems.
  • Solve complex learning systems tasks, such as optimization, prediction, and classification.
  • Understand and make use of probability theory to build and reason with learning systems
  • Appreciate the difference between correlation- and causality-based models
  • Build and reason with complex causal models in the form of Bayesian networks.
  • Cast traditional problems in a learning systems perspective

Contents

This course gives an advanced introduction to techniques for constructing learning systems, such as bandit algorithms, learning automata, Tsetlin machines, Thompson sampling, and various forms of Bayesian learning and reasoning. Furthermore, the course covers techniques within intelligent data analysis and pattern recognition. Applications within natural language processing, image analysis, and optimization will be emphasized.

Teaching and learning methods

The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. The workload for the average student is approximately 200 hours.

Examinations

Graded portfolio assessment. Information about the content of the portfolio will be given in Canvas at the start of the semester for each seminar.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes, if there are places available

Admission Requirement if given as a free-standing course

Admission requirements for the course are the same as for the master’s programme in ICT.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 8:24:19 AM