Syllabus for Artificial Intelligence

Artificiell intelligens

A revised version of the syllabus is available.

  • 5 credits
  • Course code: 1DL340
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2009-03-12
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2009
  • Entry requirements:

    120 credits with mathematics 20 credits and Computing Science 30 credits including a second course in Computer Programming.

  • Responsible department: Department of Information Technology

Learning outcomes

In order to pass, the student must 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, learning),
  • describe and use search methods, expert systems, statistical methods and simple methods for learning,
  • discuss different definitions of AI, and relate those to the history of AI.

Content

Heuristic search, knowledge representation, expert systems, learning systems.

Applications of AI, for instance in computer games.

Instruction

Lectures and labs.

Assessment

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

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.

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