Applied Artificial Intelligence
Applied Artificial Intelligence
Table of Contents
Study Facts
- Prog. CodePAAI2-FC
- NQF Level5.2
- Credits120
- Valid fromH26
- Versionv1.0 (2025-11)
- Study ModeFull-time, Part-time
- Program Manager
Applied Artificial Intelligence (AAI) is an applied discipline within computing that focuses on developing intelligent digital systems through data-driven methods. The field integrates machine learning, data science, and software engineering to create solutions that can analyse information, recognise patterns, automate processes, and support decision-making. The discipline builds on established heuristics and modern AI techniques to deliver reliable and ethical intelligence in digital products.
Applied AI differs from traditional software development, which depends on deterministic logic and predefined rules. AI systems instead learn from data through iterative training, evaluation, and optimisation. This adaptive approach enables continuous improvement, rapid insight generation, and advanced automation across a wide range of domains.
The programme combines theoretical understanding with practical training to prepare candidates for professional work in AI and machine learning. Students gain insight into core AI principles, data preparation, statistical methods, and machine learning techniques, alongside hands-on experience in deep learning, natural language processing, agent-based systems, and generative models. They also work with cloud-based workflows, big data, and MLOps practices, supported by an introduction to responsible and transparent AI development.
Graduates from the programme are equipped with the technical competence and analytical mindset required to design, implement, and deploy intelligent systems in modern IT environments. The education provides a foundation for roles such as AI/ML engineer, applied AI specialist, data scientist, or software developer, and supports lifelong learning and continued professional development within the field of artificial intelligence.
The Digital Classroom
All students at Noroff have access to a digital classroom, referred to as the learning platform. Here the student can access relevant academic and practical information about the study programme. The learning platform also contains learning content, activities, delivery deadlines, work requirements and assessments for every course.
Online
Online studies are flexible since students can study from anywhere and at their own pace according to the academic progression and scheduled deadlines. Students access their learning material for each course through the learning platform, and discussion forums are used for communication between fellow students and teachers. Lectures and live-stream sessions are not a part of the primary offer but may be provided as an additional activity depending on the course.
Campus
As part of the campus community, students will have access to on-site teachers, guest lecturers, and other students during their learning journey. Students on campus study in modern working environments and have access to professional equipment for practical training.
Vocational education at Noroff can expand career opportunities and lay lifelong learning foundations. Throughout the programme, students will familiarise themselves with key competencies relevant to industry employment.
Career opportunities
After graduation, the candidate may qualify for work within these areas:
- Data scientist
- Software developer
- AI/ML engineer
- AI Application Developer
- Machine Learning Technician
- Automation Specialist
The Norwegian Qualifications Framework for lifelong learning (NQF) defines the levels of qualifications in the Norwegian educational system. These levels describe the knowledge, skills, and competence a learner is expected to achieve as a result of a learning process. Categories in NQF are defined as:
Knowledge: Understanding theories, facts, principles, procedures in the discipline, subject area and/or occupation.
Skills: Ability to utilise knowledge to solve problems or tasks (cognitive, practical, creative and communication skills).
General Competence: Ability to independently utilise knowledge and skills in different situations.
After graduation from this programme, students have acquired the following learning outcomes:
The candidate...
- has knowledge of industry-relevant concepts, theories, models, processes, and tools used in applied machine learning and practical development for artificial intelligence.
- has knowledge of processes and methods that are used to solve data-driven problems within the field of applied machine learning.
- has knowledge of IT industry-relevant processes and tools used in the development of data acquisition and cloud services relevant to the field of artificial intelligence.
- can update their knowledge related to fundamental programming, data mining, data preprocessing, and programming for machine learning within the field of applied artificial intelligence.
- can assess their work in relation to the applicable industry norms and ethical standards, ensuring high-quality and responsible solutions in the field of applied machine learning and artificial intelligence.
- is familiar with the history, heuristics, and societal role of applied machine learning and artificial intelligence.
- has insight into their own opportunities for professional development within the field of applied machine learning and practical development for artificial intelligence.
The candidate...
- can apply knowledge to identify and solve problems using machine learning.
- can explain their choices in the artificial intelligence field and reflect on their practice, adjusting approaches and techniques under supervision to improve outcomes.
- can reflect on their choice of applicable methods and tools to design, implement, and optimise artificial intelligence models.
- can reflect on their practice to solve complex, real-world problems within the fields of applied machine learning and artificial intelligence.
- masters relevant tools and techniques for programming applications that utilise machine learning.
- can ensure their work is applicable in solving complex, real-world IT problems and can adjust it under supervision.
- can find and reference relevant information and artificial intelligence-related materials, effectively applying them to issues or project needs.
- can reflect on their skills to deploy artificial intelligence models into production and adjust them under supervision.
- can explain their choices in managing the life cycle and relevance of an artificial intelligence model through applicable tools and processes.
The candidate...
- understands the ethical guidelines and codes of conduct that apply in machine learning.
- can plan and carry out applied machine learning and artificial intelligence projects alone or as part of a group, in accordance with ethical requirements and industry principles.
- can carry out work based on the needs of selected target groups, adapting machine learning solutions and communication to ensure relevance, usability, and ethical alignment.
- can build relations with their peers across discipline boundaries and with external target groups within the fields of applied machine learning and artificial intelligence.
- can exchange professional viewpoints with peers and participate in discussions on the development of good practices and projects within the fields of applied machine learning and artificial intelligence.
- can contribute to organisational development by applying artificial intelligence insights to improve processes, support data-driven decision-making, and promote responsible and effective use of machine learning within the organisation.
Noroff offers an engaging student-active learning experience that prepares students for professional working life through unique and industry-relevant learning activities, including flexible and independent self-study. Guidance and feedback from teachers support students' learning journey.
All learning activities are aligned with the current learning outcomes, designed to promote a holistic understanding of key issues and challenges within each subject area. By fostering critical thinking, creativity, collaboration, and communication, students will develop lifelong learning skills.
Learning activities can vary for campus and online delivery and are composed of theoretical and practical approaches, providing students with the best possible outcome for each course.
For all online studies, English is the primary language for teaching. English can also be used as the teaching language on some campuses.
Assessment consists of compulsory activities and exams. These impact the student’s learning significantly and concludes if the student has achieved the intended learning outcome and, if so, at what level. Assessments include summative and formative methods depending on the content of the learning outcome of each course. Assessments for each course are described in the course descriptions.
Information about equipment requirements is available here: Programme information.
Online students are required to purchase and maintain their equipment.
There are three ways to meet the admission criteria and be enrolled as a student:
1. By upper secondary education (videregående skole).
- Higher education entrance qualification from Norway or abroad.
2. By Norwegian vocational upper secondary education.
- Documented vocational qualifications diploma (yrkeskompetanse) within: Space Technology Technician (romteknolog).
- Documented craft certificate (fag og svennebrev) within: Avionics Technician, Automation Technician, Construction Machinery Mechanic, Car Mechanic (Light Vehicles), Car Mechanic (Heavy Vehicles), Drilling and Maintenance Operator, Well Operator (Electrical Cable Operations), Well Operator (Mechanical Cable Operations), Computer Electronics Technician, Dimensional Controller, Drone Operator, Electrician, Electro Repair Technician, Energy Lineworker, Energy Operator, Laboratory Technician, Field Utility Operator (FU-operator), ICT Service Technician, IT Operations Technician, IT Developer, Industrial Mechanic, Production Electronics Technician, Space Technology Technician, Signal Technician, Ventilation Technician.
(avioniker, automatiker, anleggsmaskinmekaniker, bilmekaniker, lette kjøretøy, bilmekaniker, tunge kjøretøy, bore- og vedlikeholdsoperatør, brønneoperatør, elektriske kabeloperasjoner, brønneoperatør, mekaniske kabeloperasjoner, dataelektroniker, dimensjonskontrollør, droneoperatør, elektriker, elektroreperatør, energimontør, energioperatør, faglaborant, fu-operatør, ikt-servicefaget, it-driftstekniker, it-utvikler, industrimekaniker, produksjonselektroniker, signalmontør, ventilasjonstekniker)
3. Prior learning and work experience.
More information about admission requirements is available on our webpage under Admission Requirements.