ACIT4040 Applied Artificial Intelligence Project Course description

A real artificial intelligence project will be carried by a large team of students. A practical application will be targeted using state-of-the-art methods and tools. The students will construct a working system from scratch, implementing machine learning components as well as using existing tools. The students are involved in the entire process, starting from earlier design choices to the AI system completion. Examples of tasks may include speech processing and image recognition, robots or drones navigation, self-driving vehicles, and chatbots.

Through this course, the students will gain an in-depth understanding of "AI in practice", as opposed to "AI in theory" or "AI on toy problems".

After completing the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• can assess knowledge on function, symptoms and health-related quality of life relating to the most common MSDs

Skills

The student

• can apply and critically reflect on knowledge relating to diagnostics, occurrence, risk factors, mechanisms and pathways when working with patients with the most common MSDs
• can apply and critically reflect on the use of guidelines for treating the most common MSDs in clinical practice
• can analyse and discuss the results of assessments and examinations of children/adults/older adults with the most common MSDs

General competence

The student

• can implement up-to-date knowledge on diagnostics, examination and treatment/prevention of the most common MSDs
• can communicate up-to-date disciplinary knowledge to collaborative partners and the population at large

No formal requirements over and above the admission requirements.

Upon successful completion of the course:

Knowledge

The student:

• understands why, when and how to use AI methods in realistic problems that they may encounter in their technical careers
• knows how to produce the necessary technical documentation
• understands how to manage a project in its expertise domain

Skills

The student can:

• work in a large group with a vaguely defined problem statement
• assess different frameworks and tools for artificial intelligence in given contexts
• build systems that realise aspects of intelligent behaviour
• take part in the design and implemention of a relatively large AI project
• debug AI applications and correct bugs at a system level (integration)

General competence

The students can:

• work in a project within their specific expertise area
• make decisions based on limited information
• tolerate previous decisions when they turn out to be suboptimal and can evaluate them when better information becomes available

The project work will be carried out in groups of a size suited for the assignment and focused around the relevant laboratories at OsloMet. The groups are relatively large, with 5-10 students.

The following required coursework must be approved before the student can take the exam:

• Minimum 80% attendance in workshops
• A midterm group presentation

Group Project Report (5-10 students) between 10000 and 25000 words.

The exam grade can be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Language of instruction: Norwegian

This course builds on the course MAMUS4100. The course presents the knowledge basis for diagnostics, occurrence, risk factors, mechanisms, pathways and treatment for the most common forms of musculoskeletal conditions (MSDs). It takes a more in-depth look at the existing knowledge-basis for examination, treatment and preventive measures for the most common MSDs. The topics will be presented from a life course perspective.

Grade scale A-F.

The student must have been admitted to the Master’s Programme in Health Sciences and hold authorisation as a physiotherapist.