Course syllabus
Autumn 2018
Course syllabus, Autumn 2018
Title
Artificial Intelligence
Swedish title
Artificiell intelligens
Course code
DA272A
Credits
7.5 credits
Grading scale
UV / Fail (U), Pass (G) or Pass with Distinction (VG)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Syllabus valid from
2017-08-28
Syllabus approval date
2017-05-04
Level
Basic level
Entry requirements
- The equivalent of English 6/English B in Swedish secondary school.
- At least 30 credits in Computer Science, including 15 credits of Object Oriented Programming.
Main field
Computer Science
Progression level
G1F / First cycle, has less than 60 credits in first-cycle course/s as entry requirementsProgression level in relation to degree requirements
This course is included in the main field of Computer Science at level 31-60 credits, and is offered as elective or single subject course.
Course objectives
The aim of the course is to introduce the field of Artificial Intelligence (AI), as well as the basic concepts and techniques that are used within the field. In addition, the course will develop insights into some of the application areas where artificial intelligence plays an important role.
Course contents
The course contains the following parts:
- Introduction to AI
- Agent technology
- Problem solving (including search methods)
- Knowledge representation and logic
- Machine learning
- Applications
Learning outcomes
Knowledge and understanding
On completion of the course the student shall:
- demonstrate understanding of AI and of the basic concepts and methods that are included in the field, as well as being able to show knowledge within the field
Skills and abilities
On completion of the course the student shall:
- demonstrate ability to implement AI-based solution methods; both individually and together with others
Judgement and approach
On completion of the course the student shall:
- for a given problem demonstrate ability to suggest AI-based solution methods, as well as being able to assess the suitability of different methods
- show ability to identify, formulate, and categorize problems that are suitable to approach using different types of AI-based methods
Learning activities
Lectures, seminars, instructor-led computer labs, and individual studies.
Assessment
Requirements for Pass: Passed on written examination 3 credits and passed on all lab examinations 4,5 credits.
Requirements for Pass with distinction: Pass with distinction on the written examination and passed on all lab examinations.
Course literature and other study material
Main literature:
- Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. 3.,[updated] ed. Boston: Pearson Education. ISBN-10: 0136042597
- Wooldridge, Michael J. (2009). An introduction to multiagent systems. 2nd ed. Chichester, U.K.: John Wiley & Sons. ISBN-10: 0470519460
- Collection of articles and chapters
Reference literature:
- Witten, Ian H., Frank, Eibe & Hall, Mark A. (2011). Data mining: practical machine learning tools and techniques. 3. ed. Burlington, MA: Morgan Kaufmann. ISBN-10: 0123748569
Course evaluation
The university provides all students who are participating in, or have completed, a course to express their experiences and views on the course through a course evaluation which is organized at the end of the course. The university will collate the course evaluations and provide information about their results and any actions prompted by them. The results shall be made available to the students. (HF 1:14).
Interim rules
When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.
Additional information
The following generic skills are practised in the course:
• Problem Solving
• Ability to work in a team
• Ability to present work orally and in writing