Course syllabus

(PLEASE consider the details below as preliminary until the course begins.)

(If this page is the only page you can see, and you are currently participating in the course, you have not been properly registered. Then please send a mail to Ashkan Panahi (see below for the mail address), and we will register you!)

Course-PM / Syllabus

DAT410 / DIT728 DAT410 / DIT728 Design of AI systems lp3 VT22 (7.5 hp)

Course is offered by the department of Computer Science and Engineering


The purpose of the course is to explain how some different well-known AI-systems work, provide insight in how such systems are built, and practice to develop such systems. The course takes a broad perspective and includes related areas such as data science, algorithms and optimization as appropriate.

The main structure of the course is given by the weekly modules:

  1. AI problem solving

  2. Recommendation systems

  3. AI tools

  4. Natural language processing

  5. Diagnosis systems

  6. Games and planning

  7. Dialogue systems and question answering
  8. Your own mini-project

There may additionally be single lectures that are not connected to a particular module.

The learning objectives are found in the official course plan. The course plans for the different course codes are essentially the same.

For detailed information about the modules see the Modules page.

Contact details

Ashkan Panahi (course responsible) ashkan.panahi at
Fredrik Johansson (teacher in several modules) fredrik.johansson at
Richard Johansson (teacher in the NLP module) richard.johansson at

Emil Carlsson (TA) caremil at
Lena Stempfle (TA) stempfle at
Christopher Kolloff (TA) kolloff at

Student Representatives

Johan Abrahamsson                               

Joakim Burman                                         

Emanuel Hedlin                                               

Emil Holmberg                                

Adnan Fazlinovic                 


This is an online course, and all lectures and supervision will be in Zoom. Some lectures will be live and some will consist of a combination of prerecorded clips and a live Q&A session. The schedule may vary slightly from week to week, see the TimeEdit schedule and specific information for each module. Below you can find a (preliminary) overview of the course:


Schedule-crop copy-1.png


You can find the zoom link for the lectures at the top of the home page. For supervision you will need to create your own zoom link and book a time slot, see the separate module instructions.

Examination and grading

The course is examined continuously through the module submissions. You pass the course by passing every module in the course.

The first two modules of the course are simply pass/not pass. The remaining modules are graded based on a qualitative assessment on the scale Sufficient, Good and Very Good related to the Chalmers and GU grades in the following way:

- Sufficient (3/G/50 in Canvas)
- Good (4/G/70 in Canvas)
- Very Good (5/VG/90 in Canvas)

The Canvas numbers are a way to numerically encode the qualitative grade in the Canvas grade field, and have no direct relationship to any proportion of correctly answered questions. For a normal Sufficient grade we would write 50, but the interval 40-59 is possible - the lowest possible passing level for a module is 40. As other examples, 75 indicates an upper-level "Good" and 80 indicates a borderline grade between Good and Very Good. The Canvas code 8 is used to indicate that the submission has not yet passed.

The final grade is based on a weighted sum of the graded modules. The final grade limits in Canvas are 40 (Sufficient/3/G), 60 (Good/4/G) and 80 (Very Good/5/VG).. If you are close to a grade boundary, you can discuss with the main teacher at the end of the course, and you will be given an opportunity to improve (there is no point in doing this as the course proceeds, only at the end). At GU, only final grades G and VG are available. Please note that before all assignments have passed, the final Canvas score may be misleading.

If you should not complete the course in time, and need to come back next year, it is in your best interest to keep copies of your solutions to enable future assessment.

Course literature

There is no compulsory course literature. Reading instructions will be provided in connection to the modules.

Changes made since the last occasion

No major changes have been made since the last occasion. 


Course summary:

Date Details Due