Applied Machine Learning
Applied Machine Learning
Table of Contents
Study Facts
- Prog. CodePAML
- NQF Level5.1
- Credits60
- Valid fromH25
- Version1.0
- Study ModeFull-time, Part-time
- Program Manager
Applied Machine Learning is emerging as a new discipline combining aspects of computer science, software development with Artificial Intelligence (AI) and data storage with data manipulation and visualisation. The programme aims to make sense of the growing amounts of massive data, known as “Big Data”. Applied Machine Learning relies on patterns and inference instead of explicit instructions. It has emerged based on the ability to use computers to probe the data for structures otherwise not visible. Applied Machine Learning provides an increased level of automation which replaces time-consuming human activities through automatic techniques. This allows for improved accuracy and efficiency by discovering and exploiting any regularities in the data.
Applied Machine Learning provides candidates with the competence and capacity to apply machine learning techniques to analyse, understand, and convert data to data products. The programme focuses on the core concepts of Machine Learning through a combination of learning methods and activities. Education is also a stepping-stone for further education as it builds on the foundations of data analysis and computer systems.
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
The Norwegian Qualifications Framework for lifelong learning (NQF) defines the levels of qualifications in the Norwegian educational system. These levels because 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 processes and methods that are used to solve data-driven problems
- has knowledge of processes tools that are used for programming with Python
- has knowledge of data collection and preparation that is used for Machine Learning tasks
- has knowledge of tools, development methodologies and processes that are used in Machine Learning applications
- can update his/her knowledge related to data mining, programming and machine learning
- has a knowledge of the IT industry and is familiar with the importance of Machine Learning
- understands the importance of effective and situation-appropriate data visualisations for communicating the outcome of Machine Learning
The candidate:
- can apply knowledge to identify and solve problems using Machine Learning
- masters descriptive statistical techniques and tools to evaluate and prepare data for Machine Learning modelling
- masters relevant tools and techniques for programming applications that utilize Machine Learning
- masters relevant tools, materials and techniques to solve real-world IT problems
- can find information relevant to developing Machine Learning applications
- can study a data problem situation and identify code and optimisation issues and what measures need to be implemented to solve the problem
The candidate:
- understands the ethical guidelines and codes of conduct that apply in Machine Learning
- can carry out Machine Learning projects using problem that can be solved using applied Machine Learning
- can build relations with his/her peers across discipline boundaries and with external target groups
- can develop Machine Learning applications using programming languages
- can develop work methods and present the results of Machine Learning applications
| Course code | Course name | Semester | Weeks | Hours | Credits |
|---|---|---|---|---|---|
| FM1AZPL05 | Problem Based Learning | 1 | 3 | 126 | 5 |
| FM1AZPR10 | Introduction to Programming | 1 | 5 | 210 | 10 |
| FM1AZDM10 | Introduction to Data Mining | 1 | 5 | 210 | 10 |
| FM1AZDP05 | Data Pre-Processing | 1 | 4 | 168 | 5 |
| FM1AZML75 | Programming for Machine Learning | 2 | 5 | 210 | 7.5 |
| FM1AZCI75 | Computational Intelligence | 2 | 5 | 210 | 7.5 |
| FM1AZDV05 | Data Visualisation | 2 | 3 | 126 | 5 |
| FM1AZP110 | Exam Project | 2 | 8 | 336 | 10 |
| Total | 38 | 1596 | 60 | ||
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: dataelektronikerfaget, automatiseringsfaget, IT-driftsfaget og IT-utviklingsfaget etc.
- Documented craft certificate (fag og svennebrev) within: automatiker, dataelektroniker, IT-driftstekniker, IT-utvikler, telekommunikasjonsmontør etc.
3. Prior learning and work experience
More information about admission requirements is available on our webpage under Admission Requirements.