Design of AI systems

The course can be part of the following programmes:

1. Computer Science, Master's Programme (N2COS)
2. Applied Data Science, Master's Programme (N2ADS)
3. Mathematical Sciences, Master's Programme (N2MAT)

The student must have successfully completed courses in:

• 7,5 hec mathematical thinking (DIT025 or DIT856 or equivalent) or a course in basic mathematics (containing e.g. calculus, linear algebra, discrete mathematics)
• 7,5 hec mathematical statistics (e.g. MSG810 or DIT862 or similar)
• 7,5 hec programming in a general-purpose language (e.g. Python/Java/C or similar),
• An introductory course in Data Science and/or AI, for examPle DIT852 or DIT405 or equivalent.

Applicants must prove knowledge of English: English 6/English level 2 or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.

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.

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.

Competence and skills

• 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.

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

Language of instruction: English

Assignments and mini-projects.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

Sub-courses

1. Assignments, 7.5 credits
   Grading scale: Pass with Distinction (VG), Pass (G) and Fail (U)

To pass the course, the assignments and mini-projects must pass. To get a higher grade than Pass, a higher weighted average from the grades of the assignments and mini- projects is required.

The course is evaluated through meetings both during and after the course between teachers and student representatives. Further, an anonymous questionnaire is used to ensure written information. The outcome of the evaluations serves to improve the course by indication which parts could be added, improved, changed or removed.

We strongly recommend to also take a course in Machine learning, for example DIT866or similar, or that such a course is taken in parallel alongside this course.

The course is a joint course together with Chalmers.