Course syllabus

Course PM

Course name: Introduction to Artificial Intelligence

Course code: TIN175/DIT411

Credits: 7.5 (7,5 hp)

Course edition: Spring 2019 (VT19)

Course provider: Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg

Revised January 15th, 2019


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 an overview of some basic AI algorithms and an understanding of the possibilities and limitations of AI.


The course gives an introduction to the subject of AI and has two main purposes.

The first purpose is to give an understanding of the different sub-areas of AI. This is done by reading literature within different AI areas, by summarizing and by discussing the literature in writing.

The second purpose is to teach basic concepts and algorithms of AI and how they can be used to solve interesting AI problems. The following topics are included:

  • soft aspects of AI, including sustainability and ethical aspects
  • supervised learning, including deep neural networks
  • unsupervised learning, including autoencoders and word embeddings
  • reinforcement learning, including deep Q-learning
  • classical search, including uninformed and informed algorithms
  • adversarial search, including algorithms for chess and backgammon
  • constraint satisfaction problems, including local search methods.

Learning objectives

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

Knowledge and understanding:

    • Explain basic concepts of machine learning and classical AI
    • Compare advantages and disadvantages of some basic AI algorithms
    • Account for the historical development, current situation and future prospects for some sub-area of AI.

Skills and abilities:

    • Choose appropriate algorithms for solving given AI problems in a memory- and time-efficient manner.
    • Implement efficient AI algorithms in a suitable programming language.
    • Summarize scientific progress and ethical issues.

    Judgement and approach:

      • Analyze and critically discuss soft aspects of AI.
      • Summarize and constructively criticize scientific texts.


    The course consists of three main sub-courses, of which two are done in groups of preferably 5 students. The groups are selected well before the group work begins.


    Here is the schedule of the course in TimeEdit. There are Lectures and Tutorials in the schedule. Tutorials are for meetings with the TAs that are usually booked in advance. Please note the following dates:

    Activity Date
    Project groups formed and topics selected 4 February
    Written exam 19 February
    Group submission: project and essay 18 March
    Oral group exam: project and essay 19/20/22 March
    Written re-exam 24 April

    Note that the dates of the exam and the re-exam do not appear in the TimeEdit schedule. Instead they are announced in the Student Portal.

    Course material

    The course material consists of texts, images, and videos. All of it is available in electronic form free of charge.

    Here is a list of the course material.

    Contact details

    Examiner, course responsible, and teacher

    Teaching assistants/supervisors

    Student representatives


    The main source of information about the course is the Canvas learning platform. There will be opportunities to communicate with the teachers and TAs in connection with lectures and supervision sessions. You can also contact the teachers via email, but please do that only if it is necessary.

    Each student needs to have access to a laptop for programming purposes. Missed deadlines must be reported to the teachers one day after the deadline at the latest.


    You have to pass all three sub-courses to pass the course. More information about passing the sub-courses and their grading criteria can be found on the pages describing the three sub-courses.


    All sub-courses are graded U/3/4/5 for Chalmers students and U/G/VG for GU students. The final grade is decided like this:

    • GU: To get final grade VG, you need a VG grade on at least two sub-courses.

    • Chalmers: The final grade is the average of the sub-course grades, weighted by the size of the sub-course, rounded like this:

      Weighted average Final grade
      < 3.65 3
      3.65–4.50 4
      > 4.50 5

    Note that the final grades on all sub-courses are individual! This means that you can get a higher or lower grade than what your other group members will get, depending on your personal contributions to the group work. To determine that we look at the your activity and knowledge during the supervision and oral exam sessions. We might also look at the commit history of the programming project.


    This is a joint Chalmers/GU course. It has two different course codes and two different course plans, but in reality it is exactly the same course:

    Comparison to the 2018 edition

    The course has been changed in a number of ways compared to the 2018 edition.

    Changes to the Lecture part

    • The theoretical content is broader now and gives an overview that covers certain parts of classical AI and certain parts of machine learning.
    • The number of lectures on classical AI have been reduced, whereas the number of lectures on machine learning has been increased.

    Changes to the Programming project

    • The project groups are primarily formed by the students and the project topics are selected by the groups and cleared by the TAs.

    Changes to the Essay part

    • The essay will be written by the project group
    • The topic of the essay will relate to the project. More specifically it will include
      • a rough description of your programming project
      • an overview of related work in the field
      • an analysis of possible extensions of your work 
      • a discussion of the social, ethical, economical, and societal aspects of the work (when relevant)
    • No peer reviews will be given on the essays.

    Course summary:

    Date Details Due