Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED L
Date of Decision: 2023-04-20
Elective for: L5-gi
Language of instruction: The course will be given in English
The overarching aim of the course is to introduce the student to new paradigms in data management with special focus on artificial intelligence (AI) and machine learning (ML) and their application in GIS and remote sensing.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
The course starts with a general introduction to the concept AI and its different components with a focus on GIS-applications. This is followed by modules with a focus on optimisation of data processing, machine learning and simulation techniques for applications in both GIS and remote sensing. Main focus for the course is technical knowledge and technical proficiencies that are aiming to that the student should be able to apply AI in different situations but aspects of ethics and public benefits are also treated in lectures during the course.
Course design: The teaching consists of lectures, practical exercises, seminars and a final project assignment that is carried out individually or in groups. Each lecture theme is highlighted with practical exercises that, based on key elements, expands and deepens the understanding of the theoretical material. Through the exercises, the student gets ability to apply AI on different spatial problems to develop solutions. Both exercises and seminars aim also to deepen the students' commitment in their own learning process. Participation in exercises, seminars, laboratory sessions and project work, as well as associated parts, is compulsory.
Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Examination takes place in the form of written assignments, exercises, seminars (1 credit) and quizzes (1.5 credits) during the course and through a final project work (5 credits).
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
To pass in the whole course passed results on all written assignments, exercises seminars, quizzes and project work and participation in all compulsory components. Written assignments, exercises, seminars are assessed according to the grading scale Fail, Pass, while the project assignment is assessed according to the grading scale Fail, 3, 4, 5. The final grade are decided through grade on the project assignment.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Parts
Code: 0123. Name: Written Hand-ins and Exercises.
Credits: 1. Grading scale: UG. Assessment: Passed exercises and assignments. Contents: Compulsory practical exercises, seminars and written assignments.
Code: 0223. Name: Quizzes.
Credits: 1,5. Grading scale: UG. Assessment: Passed quizzes. Contents: Compulsory quizzes.
Code: 0323. Name: Project Work.
Credits: 5. Grading scale: TH. Assessment: Graded (TH) project assignment, which will also be deciding the course grade.
The number of participants is limited to: No
The course overlaps following course/s: NGEN27
Course coordinator: Ali Mansourian, ali.mansourian@nateko.lu.se
Course administrator: Karin Larsson, karin.larsson@nateko.lu.se
Examinator: Ali Mansourian, ali.mansourian@nateko.lu.se
Course homepage: https://www.nateko.lu.se/xxx - will be published here