The course is connected to the following study programs

  • Computer Engineering, Bachelor's Programmme
  • Artificial Intelligence, 5-year master programme

Teaching language

English

Course contents

This course will cover the basic components of building and applying machine learning with an emphasis on practical applications. It will provide a basis in concepts such as training algorithms and evaluation before setting into production.

This course will also introduce students to practical artificial intelligence tools and architectures, including distributed approached and GPU-based solutions.

Learning outcomes

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

  • develop practical artificial intelligence software
  • train and validate machine learning algorithms
  • develop simple distributed and GPU-based artificial intelligence systems

Teaching 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 work load for the average student is approximately 200 hours

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.

Assessment methods and criteria

Graded portfolio assessment. Information about the content of the portfolio will be given in Canvas at the start of the semester for each seminar. There will not be arranged a postponed exam for the portfolio.

Last updated from FS (Common Student System) June 30, 2024 1:55:28 AM