Artificial Intelligence, 7.5 credits

Aims and objectives

General aims for first cycle education

First-cycle courses and study programmes shall develop:

• the ability of students to make independent and critical assessments
• the ability of students to identify, formulate and solve problems autonomously, and
• the preparedness of students to deal with changes in working life.

In addition to knowledge and skills in their field of study, students shall develop the ability to:

• gather and interpret information at a scholarly level
• stay abreast of the development of knowledge, and
• communicate their knowledge to others, including those who lack specialist knowledge in the field.

(Higher Education Act, Chapter 1, Section 8)

Course objectives

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.

Main content of the course

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

Teaching methods

Teaching is done in the form of computer laboratory work and lectures.
If there is a need, the lectures can be supplemented with theoretical exercises.

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.

Examination methods

Theory, 4.5 credits (Code: A001)
Written examination

Laboratory Work, 3 credits (Code: A002)
Oral and written presentation of project work, written assignment on selected ethical issues. Project tasks and assignments are presented individually or in groups according to the teacher's instructions.

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

For further information, see the university's local examination regulations (in Swedish).

Comments on examination methods

A retake will be scheduled to take place within eleven weeks of the regular examination

Grades

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 prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

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

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

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

Laboratory Work
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

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

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 (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Transitional provisions

The course can be given in English.

Reading list and other teaching materials

Required Reading

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