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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
TIN175 - Introduction to Artificial intelligence
Introduktion till artificiell intelligens
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: MPALG
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology

The course round is cancelled. For further questions, please contact the director of studies MPALG: COMPUTER SCIENCE - ALGORITHMS, LANGUAGES AND LOGIC, MSC PROGR, contact information can be found here

Teaching language: English
Application code: 02126
Open for exchange students: No
Block schedule: D+
Maximum participants: 85

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0117 Examination 2,5 c Grading: TH   2,5 c   06 Apr 2020 am DIST  
0217 Project 3,5 c Grading: TH   3,5 c    
0317 Written and oral assignments 1,5 c Grading: TH   1,5 c    

In programs

TIDAL COMPUTER ENGINEERING, Year 3 (compulsory elective)


Claes Strannegård


TIN173   Artificial intelligence TIN174   Artificial intelligence


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

To be eligible for the course students should have successfully completed courses corresponding to 60 hec within the subject Computer Science, or equivalent, including a second 7.5 hec programming course (TDA552, TDA342, or equivalent) and a 7.5 hec course in data structures (DAT037, TDA416, or equivalent).

This is an advanced course: We assume academic maturity and a willingness to explore independently. The student should have the ability to complete a sizeable programming project.


Artificial Intelligence (AI) studies how computers can accomplish tasks that were traditionally thought to require human intelligence. The aim of this course is to give a deepened understanding of the possibilities and the limitations of AI methods.

Learning outcomes (after completion of the course the student should be able to)

On successful completion of the course the student will be able to:

Knowledge and understanding:

  • Explain basic concepts and algorithms for cost-based search, planning and constraint satisfaction problems (CSP).

  • Compare advantages and disadvantages with different algorithms for search, planning and CSP.

  • Account for the historical development, current situation and future prospects for some subarea of AI.

Skills and abilities:

  • Choose appropriate algorithms for search, planning and CSP to solve given AI problems in a memory- and time-efficient manner.

  • Implement efficient algorithms for search, planning and CSP in a suitable programming language.

  • Summarise scientific progress and ethical issues.

  • Write scientific texts.

Judgement and approach:

  • Analyse and critically discuss ethical issues within AI.

  • Review and constructively criticise scientific texts.


Artificial Intelligence (AI) studies how computers can accomplish tasks that were traditionally thought to require human intelligence. This course gives an introduction to the subject and has two main purposes.

The first purpose is to give an understanding of which sub-areas there are within AI, their historical development and which ethical issues that can arise within different sub-areas. This is done by reading literature within different AI areas, by summarising and discussing the literature in writing, and by reviewing essays by other students.

The second purpose is to teach basic concepts and algorithms for heuristic search, planning and problem solving, including their usage, and how they can be used to solve interesting AI problems. The following algorithms and concepts are included:

  • general search problems ¿ weighted and unweighted graphs, graph search, tree search, search trees

  • different classes of search and planning problems ¿ complete and incomplete information, deterministic and nondeterministic problems

  • standard algorithms for deterministic search and planning with perfect information ¿ uninformed search, informed search, local search

  • search with incomplete information, nondeterministic problems, and problems with multiple agents

  • constraint satisfaction problems (CSP)

  • heuristics for informed search and for CSP


The students form project groups of 3-5 persons, and every group is assigned a supervisor, a programming project and an essay topic. The forms of teaching are group supervision, essay writing, program development in groups, and peer review on essays by other groups. Furthermore there are theoretical lectures, practical assignments and written examination.


See separate literature list.

Examination including compulsory elements

The course is examined by:

  • an individual written examination (2.5 hec)

  • a programming project carried out in groups of 3¿5 students, with oral presentation (3.5 hec)

  • a written essay in groups of 3¿5 students, with peer review (1.5 hec)

  • To pass the group sub-courses, the student must participate actively during supervision, presentation and in the group¿s internal planning, and must make essential and measurable contributions to the final outcome (the program and the essay). To pass the essay sub-course, the student must furthermore read and actively discuss essays written by other project groups.

    If the student is failed on a group sub-course, despite participating in the group work, they will get a task to complete individually instead of in a group. If the student also fails this, they have to redo the whole sub-course in a new project group.

    The final grade of the course is based on the weighted average of the grades of the individual subcourses.

Page manager Published: Thu 04 Feb 2021.