Syllabus for Artificial Intelligence

Learning outcomes

On completion of the course, the student should be able to:

• recognise that a problem is an AI-problem,
• model AI-problems and point out an appropriate solution (for example expert systems, search algorithms, machine learning),
• describe and use search methods, expert systems, statistical methods and methods for learning,
• make judgments with regard to relevant scientific, social and ethical aspects in the application of AI,
• discuss different definitions of AI, and relate those to the history of AI.

Content

Heuristic search, knowledge representation, expert systems, machine learning including artificial neural networks and deep learning.

Applications of AI, for instance in computer games.

Instruction

Lectures and labs.

Assessment

Written exam (3 credits), assignments (2 credits) and project (2.5 credits) that are presented orally or in writing.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course cannot be included in a degree with 1DL340 Artificial Intelligence.