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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
DAT410 - Design of AI systems
Design av AI-system
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: MPDSC
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology

The course is only available for students having the course in their program plan
Teaching language: English
Application code: 87115
Open for exchange students: No
Block schedule: D
Maximum participants: 30
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0119 Written and oral assignments 7,5 c Grading: TH   7,5 c    

In programs



Dag Wedelin

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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

Mathematics (at least 30cr., including Multivariable Analysis, Linear Algebra, and Mathematical Statistics), Programming in a general-purpose language (e.g. Python/Java/C or similar (at least 7,5 cr.)), and Algorithms and/or Data Structures (at least 7,5 cr.)

An introductory course in Data Science and/or AI. A basic course in Machine Learning or that such a course is taken in parallel alongside this course.


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.

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
  • Provide an overview of different applications of AI and related areas.
  • Describe how some different well-known AI-systems work and how they are used.
  • Explain how AI approaches relate to other kinds of advanced information processing. 
Skills and abilities
  • Identify problems that can be solved with AI and other advanced computational techniques.
  • Design simpler Ai systems for different applications, including model choices and system design.
  • Implement AI systems with programming in combination with different tools and programming libraries.
Judgement and approach
  • Discuss advantages and disadvantages of different models and approaches in AI.
  • Reflect over fundamental possibilities and limitations of current AI approaches. 
  • Critically analyze and discuss AI applications with respect to ethics, privacy and societal impact.
  • Show a reflective attitude in all learning.


The course teaches design of AI systems in several different ways:
  • Reading of papers and lectures describing different AI systems and their design (eg. AlphaZero, Watson, systems for self-driving cars,…)
  • Opportunities to see and try out the implementation of different simpler AI systems. 
  • Own problem solving in the form of design and implementation of simpler AI systems.
  • Discussions about possibilities and limitations of AI, ethics and societal impact. 


Lectures and modules with exercises and mini-projects – these are mainly done in groups to two persons.


Reading in the form of papers etc. , to be presented as the course proceeds.

Examination including compulsory elements

Assignments and mini-projects. No exam.

Page manager Published: Thu 04 Feb 2021.