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, chatbots, etc.

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".

It is highly recommended that the student has taken the courses Advanced Machine Learning and Deep Learning and/or Evolutionary AI and robotics. It is also recommended to have good programming skills, such as 20ECT of previous prgramming-focused subjects.

No formal requirements over and above the admission requirements.

Upon successful completion of the course:

Knowledge

• Students will gain valuable insights into why, when and how to use AI methods in realistic problems that they may encounter in their technical careers, as well as the necessary expertise to produce necessary documentation and project management.

Skills

• The students learn to work in a large group with a vaguely defined problem statement.
• The students learn to assess different frameworks and tools for artificial intelligence in given contexts.
• The students will build systems that realises aspects of intelligent behaviour.
• The students will gain hands-on experience designing and implementing a relatively large AI project.
• The students learn debugging their applications and bug correction at a system level (integration).

General competence

• The students learn to work in a project within their specific expertise. This includes making decisions based on limited information, tolerating these decisions when they turn out to be suboptimal and evaluating them when better information becomes available.

Introduction to modern methods, techniques and tools used in projects related to the course assignment. Lectures and tutorials will be given on the tools, laboratories and facilities available at OsloMet, and their use in relation to the given assignment text, specifications, design, verification, prototyping and development. A realistic project will then be carried out where participants work together as an "applied artificial intelligence development team".

The project involves the full process from specifications, programming, testing, verification and documentation.

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 5-20 students.

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

• Minimum 80% attendance in workshops
• A final group (5-20 students) presentation.

Project report (between 10000 and 25000 words) (100%)

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 applying 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.

All aids are permitted.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Two internal examiners. External examiner is used periodically. 

Associate Professor Stefano Nichele