Applied Machine Learning

Applied Machine Learning

  • Study facts
    • Prog. Code
      PAML
    • NQF Level
      5.1
    • Credits
      60
    • Valid from
      H24
    • Dated
      14.08.2024
    • Version
      1.0
    • Study mode
      Full-time, Part-time
    • Program manager
      Bertram Haskins
About the programme

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.

Learning Environment

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.

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.

After graduation

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
Learning Outcome

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:

Knowledge

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
Skills

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
General Competence

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 Overview
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

 

Course Models
Applied Machine Learning
Teaching and Learning

Noroff offers an engaging and student-active learning experience that prepares candidates for professional working life through unique and industry-relevant teaching and learning activities governed by the current learning outcomes. Teaching and learning engage students in the learning process by promoting a holistic understanding of the different issues and challenges relevant to the subject areas. By fostering critical thinking, creativity, collaboration, and communication, students will develop lifelong learning skills. 

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. Noroff distinguishes between teacher and student-led activities. Both are equally important and tailored to each course’s educational approach. Teaching and Learning activities used in the courses are outlined in the course descriptions. 

For all online studies, English is the primary language for teaching. English can also be used as the teaching language on some campuses.

Work Requirements and Assessment

Assessment impacts 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.

A course usually consists of one or more work requirements. The most common is compulsory course assignments that assess the acquired competencies outlined in the course learning outcomes. Course assignments are assessed as Passed/Failed or graded from A to F, after which verbal or written feedback is provided. Tests can also evaluate students’ achievements and are usually used in combination with compulsory assignments.

Online studies may also require students to deliver one or more compulsory module assignments during a course. This is to follow up and support the online students’ learning path. Module assignments can be used as learning activities for campus students.

Work requirements and assessment methods for each course are described in the course descriptions.

Equipment Requirements

Information about equipment requirements is available here: Programme information.

Online students are required to purchase and maintain their equipment.

Admission requirements

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 etc. 

3. Prior learning and work experience   

More information about admission requirements is available on our webpage under Admission Requirements.