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".

Oral examination, individual.

The exam cannot be appealed.

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.

None.

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.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student has:

• an in-depth understanding of machine learning in its main forms: supervised, unsupervised, and reinforcement learning, both theoretical and applied, to solve real- lifeproblems.
• knowledge and understanding of the main concepts of deep learning.
• knowledge and understanding of some major concepts in artificial intelligence, including: complex systems (network models, cellular automata, and agent-based models) and evolutionary computing.

Skills

On successful completion of the course, the student can:

• apply techniques from machine learning to real-life problems.
• analyse data sets with the aid of machine learning algorithms.

General competence

On successful completion of the course, the student can:

• use libraries for programming deep learning algorithms such as TensorFlow.
• deploy models to relevant real-life problems.
• solve computational problem using evolutionary computing.

Each module will be taught in a series of lectures. At the end of each module, the students will be assigned a small project to be submitted within a given deadline.

Two internal examiners. External examiner is used periodically.

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

Compulsory assignments must be approved prior to the exam. The students must submit a small project at the end of each module. All five projects must be approved before examination.