Syllabus for Advanced Image Analysis
- 7.5 credits
- Course code: 1MD037
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Image Analysis and Machine Learning A1F
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2021-03-04
- Established by: The Faculty Board of Science and Technology
- Revised: 2022-10-24
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2023
120 credits including Statistical Machine Learining, a second course in computer programming, Introduction to Image Analysis or Computer-Assisted Image Analysis I. Participation in Deep Learning for Image Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
On completion of the course, the student shall be able to:
- critically evaluate and discuss scientific papers in the field of image analysis and machine learning;
- search for and critically evaluate scientific papers to obtain a deeper understanding of specific sub-fields within image analysis and machine learning;
- describe principles, methods, and algorithms within image analysis and machine learning, as well as their applications and limitations;
- integrate theory and method knowledge for image analysis and machine learning, and to use, compare and evaluated different models.
The course is based on seminars, guest lectures, reading and discussion of scientific papers. The course contents are thematically adapted to the different specialization within the Master's Programme in Image Analysis and Machine Learning, and to some extent also to the preferences of the individual students. Topics that may be included in the course include texture analysis, image registration, graph based methods.
The course also contains reading assignments, oral and written presentations, critical analysis of scientific papers and methods, discussions one ethical principles. Practical implementation and evaluation of methods for image analysis and machine learning may also be included.
Lectures, seminares, and literature studies.
Active participations in literature seminars (2.5 credits). Written and oral presentation of individual literature studies within a topic of specialization (5 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
- Latest syllabus (applies from Autumn 2023)
- Previous syllabus (applies from Autumn 2021)
The reading list is missing. For further information, please contact the responsible department.