Syllabus for Artificial Intelligence
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,
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:
- 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
- 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
- Revised: 2009-03-12
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2009
120 credits with mathematics 20 credits and Computing Science 30 credits including a second course in Computer Programming.
- Responsible department: Department of Information Technology
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.
Heuristic search, knowledge representation, expert systems, learning systems.
Applications of AI, for instance in computer games.
Lectures and labs.
Written exam (3 credits) and assignments (2 credits) that are presented orally or in writing.
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Autumn 2021)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2011)
- Previous syllabus (applies from Autumn 2009, version 2)
- Previous syllabus (applies from Autumn 2009, version 1)
Applies from: Autumn 2009
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