EPN-V2

MSL4200 Research Methods and Theory of Science Course description

Course name in Norwegian
Forskningsmetoder og vitenskapsteori
Study programme
Executive Master in Public Management
Weight
10.0 ECTS
Year of study
2023/2024
Curriculum
SPRING 2024
Schedule
Course history

Introduction

No formal requirements over and above the admission requirements.

Recommended preliminary courses

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.

Required preliminary courses

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

Learning outcomes

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.

Teaching and learning methods

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

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

Course requirements

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.

Assessment

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Permitted exam materials and equipment

Grade scale A-F.

Grading scale

Two internal examiners. External examiner is used periodically.

Examiners

TBA

Course contact person

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 20 ECTS of previous programming-focused subjects.