Artificial Intelligence, 7.5 credits

Learning outcomes

Knowledge and comprehension
After completed course the student shall be able to

• explain the principles behind intelligent systems and solutions,
• discuss the relevance of problem- and knowledge modelling,
• apply and assess solutions based on search, planning, knowledge representation and reasoning
• and other classical areas of Artificial Intelligence, and
• formulate problems and analyze solutions in modern areas such as Intelligent Agents, Probabilistic Reasoning and Machine Learning.

Proficiency and ability
After completed course the student shall be able to:

• model simple problems for the application of intelligent problem solving methods,
• apply intelligent algorithms in an appropriate programming language and suitable problem context, and
• discuss and evaluate which is the best intelligent method for solving particular problems.

Values and attitude

• After completed course the student shall have a professional relation to the context, usage and implementation of intelligent methods.

Content

The course provides a general picture of a larger selection of sub-areas within AI. These are included in theory and partly in use:

• Problem solving
• Automated reasoning
• Automated planning
• Knowledge representation
• Machine learning, and
• Social and ethical issues in AI

Examinations and grades

Theory, 4.5 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Laboratory Work, 3 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

The grade from A001 is given as a grade on the course, given that A002 is approved.

Modes of assessment

• Theory (code A001): Written exam
• Laboratory tests (code A002): Written assignments and oral examination. Presented individually or in groups according to the teacher's instructions.

The re-exam falls within eleven weeks of the regular exam.

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

Introduction to Programming, 7.5 Credits, Data Structures and Algorithms, 7.5 Credits and Object-Oriented Programming, 7.5 Credits.

For further information, see the university's admission regulations.

Other provisions

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Transitional provisions

The course can be given in English.

Reading list and other learning resources

Required Reading

Russell, Stuart; Norvig, Peter (senaste upplagan)
Artificial Intelligence, A Modern Approach
Pearson Education