Syllabus for Project in Software Development in Image Analysis and Machine Learning
Projekt i mjukvaruutveckling inom bildanalys och maskininlärning
A revised version of the syllabus is available.
- 15 credits
- Course code: 1MD036
- 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 (G), Pass with distinction (VG)
- Established: 2021-03-04
- Established by: The Faculty Board of Science and Technology
- Applies from: Autumn 2021
120 hp including 40 hp in mathematics and 60 hp in computer science, including Deep Machine Learning for Image Analysis, and Data, Ethics and Law. 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:
- account for in-depth methodological knowledge in image analysis and machine learning;
- integrate theory and methodological knowledge for image analysis and machine learning, and to use, compare and evaluate different models in realistic problem situations;
- plan and execute a project in the main area of image analysis and machine learning in a group within a given time frame;
- analyse a given problem, identify sub-problems and find and choose solution methods for these;
- independently search, evaluate and use scientific and technical information to achieve project objectives and meet the requirements specifications;
- demonstrate a solution to a project task and demonstrate whether it meets the requirement specifications;
- in writing as well as orally present the project assignment, prerequisite, argue for the chosen methodology and present the results.
The course consists of a group work on a project from outside academia or research, as well as a number of lectures on topics related to the development of software products. Examples of topics for such lectures are: Framework for software development (for example, agile methods such as Scrum), project and time planning, IP and licensing issues, business plan and market analysis, quality systems, rules and regulations, oral and written presentation, ethics. The lectures are intertwined with the introduction of the projects, problem analysis and planning. The scope of the projects is suitable for groups of approximately 6 students. After the introductory phase, the students complete the projects, and at the end of the course this is presented to the course participants and external project owners. Ethical considerations are integrated into the projects, with the goal that students should develop the ability to participate constructively in dialogue on ethical issues and motivate their choices. At a project level, we can imagine multidisciplinary teams in collaboration with other programs, which in a suitable way combine skills.
Lectures, project work.
Written report and oral presentations at the seminars.
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.