Course syllabus adopted 2025-02-13 by Head of Programme (or corresponding).
Overview
- Swedish nameTolkningsbar artificiell intelligens
- CodeTME286
- Credits7.5 Credits
- OwnerMPCAS
- Education cycleSecond-cycle
- Main field of studyEngineering Physics
- DepartmentMECHANICS AND MARITIME SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 11112
- Maximum participants80 (at least 10% of the seats are reserved for exchange students)
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
---|---|---|---|---|---|---|---|
0121 Project 7.5 c Grading: TH | 7.5 c |
In programmes
- MPALG - Computer Science - Algorithms, Languages and Logic, Year 1 (elective)
- MPCAS - Complex Adaptive Systems, Year 1 (compulsory elective)
- MPHPC - High-performance Computer Systems, Year 1 (elective)
- MPSYS - Systems, Control and Mechatronics, Year 1 (elective)
Examiner
- Mattias Wahde
- Full Professor, Vehicle Engineering and Autonomous Systems, Mechanics and Maritime Sciences
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Engineering mathematics and (preferably object-oriented) programming.Aim
The aim of the course is for the students to gain knowledge regarding interpretable methods in artificial intelligence, as well as applications of such methods, especially in high-stakes situations, for example in healthcare, automated driving, finance, and so on. The course also aims to highlight differences between interpretable systems and so-called black-box models, e.g., deep neural networks.Learning outcomes (after completion of the course the student should be able to)
Content
Organisation
The course runs over one quarter and is organized as a series of lectures combined with assignments. The assignments are carried out individually by each student.Literature
A compendium and a selection of scientific papers.Examination including compulsory elements
The examination is based on a set of assignments, solved individually by each student.The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.