Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), Rovaniemi, Autumn 2024: Machine Learning and Data Engineering 2024 Autumn
Code: R54D24S
Descriptions
This engineering programme gives you the skills to work in expert positions, or as an entrepreneur, in the field of machine learning (hereon referred as ML) and data engineering. You will study hands-on right from the beginning of your studies by following an integrated curriculum where you learn to apply your knowledge in practical real-life projects. You get project ideas from the business sector and various research -, development – and innovation projects in the home university. Most of the projects are linked to the Industry 4.0 and Industry 5.0 development and connected megatrends of automation, human-machine cooperation and digitalization of modern industry.
You get to study the main areas of ML and artificial intelligence (AI) by building your path starting from data analytics, robotics and Internet of Things technologies (IoT), into an engineer capable of implementing your own machine learning system using modern techniques. You will also learn how to work in projects and how to manage them in an international team. Simultaneously you acquire crucial ethical and sustainable development skills that are sought after in modern working life.
Your curriculum and studies consist of core competences and profiling competences. By acquiring the core competences, you build a strong foundation in intelligent systems, project management and agile methods, language skills, programming and business. Basic -, professional – and advanced professional internships guide you to the working life during your studies and enable you to put into practice what you have learned. In profiling competences, you strengthen your profile by diving deeper into the world of AI and ML by learning advanced methods for preparing your data and developing your own ML systems. Not only you learn the technical skills, but you also understand the business value of your data-driven solutions during a semester focusing solely in business development.
Machine Learning and Data Engineering programme is part of ICT engineering education in Lapland University of Applied Sciences. This education programme is part of international engineering education development network called CDIO (www.cdio.org). The network has over 50 members from 25 different countries. The themes of your academic years come from the CDIO principle, which aims at strengthening knowledge, skills and attitudes from the basis of international engineering education framework. The themes of CDIO follow the idea of process or system development. The themes are structured around academic years as follows:
- 1st year: C for Conceiving
- 2nd year: D for Designing
- 3rd year: I for Implementing
- 4th year: O for Operating
You will learn through developing. Your courses are held during day-time in Rovaniemi campus, where you will work together with your fellow students, hands-on, in projects and by carrying out individual and group assignments. You take some online courses to complement your expertise. You receive technical and general guidance from our experts both at campus and online. You carry out projects or internships each year in cooperation with the university and northern business and industry partners.
In total, your studies include 30 ECTS of internships.
The programme is 240 ECTS and the duration is 4 years. The study structure consists of:
- Core competences 180 ECTS, including internships
- Profiling competences 40 ECTS
- Thesis 20 ECTS
In addition to the study year themes in the CDIO-framework, you will have semester themes that lead you in your professional path to become an industrial machine learning expert:
- 1st semester: Orientation to Machine Learning
- 2nd semester: Data Analytics and Visualization
- 3rd semester: Internet of Things (IoT)
- 4th semester: Robotics and AI
- 5th semester: Data Engineering and Machine Learning
- 6th semester: Machine Learning and AI
- 7th semester: Business Development
- 8th semester: Emerging Technologies
Select timing, structure or classification view
Show study timings by semester, study year or period
Code | Name | Credits (cr) | 2024-2025 | 2025-2026 | 2026-2027 | 2027-2028 |
Autumn
2024 |
Spring
2025 |
Autumn
2025 |
Spring
2026 |
Autumn
2026 |
Spring
2027 |
Autumn
2027 |
Spring
2028 |
1. / 2024 | 2. / 2025 | 3. / 2025 | 1. / 2025 | 2. / 2026 | 3. / 2026 | 1. / 2026 | 2. / 2027 | 3. / 2027 | 1. / 2027 | 2. / 2028 | 3. / 2028 |
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MLDECORE24 |
CORE COMPETENCES
(Choose all) |
160 | ||||||||||||||||||||||||
AMKO045 | Start your UAS studies | 5 | ||||||||||||||||||||||||
R504D97 | Seminar: Machine Learning and Data Engineering | 5 | 5 | 5 | 5 | |||||||||||||||||||||
MLDE24MATH |
Mathematics and Natural Sciences
(Choose all) |
20 | ||||||||||||||||||||||||
R504D51 | Algebra, Geometry and Trigonometry | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D95 | Probability, Statistics and Optimization | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D58 | Linear Algebra | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504TL119 | Electromagnetism | 5 | 5 | 5 | 5 | |||||||||||||||||||||
MLDE24FIN |
Finnish Language
(Choose ects: 0) |
0 | ||||||||||||||||||||||||
VVV30 | Finnish 1 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
VVV31 | Finnish 2 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
VVV32 | Finnish 3 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
VVV33 | Finnish 4 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
MLDE24LANG |
English and Swedish Languages
(Choose ects: 0) |
0 | ||||||||||||||||||||||||
R504D126 | Professional English for ICT Engineers 1 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D127 | Professional English for ICT Engineers 2 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D128 | Professional English for ICT Engineers 3 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D61 | Swedish for ICT Engineers | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
RUOTSIS | Swedish Oral Skills | 0 | ||||||||||||||||||||||||
RUOTSIK | Swedish Written Language | 0 | ||||||||||||||||||||||||
MLDE24BUSINESS |
Business and Management
(Choose all) |
25 | ||||||||||||||||||||||||
R504D90 | Business Skills and Entrepreneurship | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D109 | Data in Business Development | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D92 | Emerging Technologies | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D125 | Industrial Engineering and Management | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D124 | Project: Startup and Business Development | 5 | 5 | 5 | 5 | |||||||||||||||||||||
MLDE24DATA |
Data and Programming
(Choose all) |
35 | ||||||||||||||||||||||||
R504D52 | Introduction to Programming | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D57 | Web Programming | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D98 | Introduction to Data Management | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D119 | Introduction to Data Analytics | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D123 | Introduction to Machine Learning Methods | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D75 | Algorithms and Data Structures | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D99 | Project: Data Analytics and Visualization | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
MLDE24INTSYS |
Intelligent Systems
(Choose all) |
30 | ||||||||||||||||||||||||
R504D96 | ICT Infrastructure and Computer Technology | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D129 | Electronics | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D118 | Electronics in IoT | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D101 | Robotics | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D100 | Project: Internet of Things (IoT) | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D103 | Project: Robotics and Artificial Intelligence (AI) | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
MLDE24INTERNSHIPS |
Internships
(Choose all) |
30 | ||||||||||||||||||||||||
R504D110 | Basic Internship 1 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D111 | Basic Internship 2 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D112 | Professional Internship 1 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D113 | Professional Internship 2 | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D114 | Advanced Professional Internship 1 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D115 | Advanced Professional Internship 2 | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
MLDE24FREE |
Free-choice Elective Studies
(Choose ects: 10) |
10 | 10 | 10 | 5 | 5 | ||||||||||||||||||||
MLDEPROF24 |
PROFILING COMPETENCES
(Choose all) |
40 | ||||||||||||||||||||||||
MLDE24PROFMOD1 |
Advanced Machine Learning Technologies
(Choose all) |
20 | ||||||||||||||||||||||||
R504D104 | Advanced Data Analytics | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D108 | Advanced Machine Learning Methods | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D121 | Reinforcement Learning | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D107 | Project: Machine Learning and AI | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
MLDE24PROFMOD2 |
Deep Learning and Data Management
(Choose all) |
20 | ||||||||||||||||||||||||
R504D120 | Cloud-Based Machine Learning | 5 | 5 | 5 | 2.5 | 2.5 | ||||||||||||||||||||
R504D80 | Deep Learning | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D105 | Advanced Data Management | 5 | 5 | 5 | 5 | |||||||||||||||||||||
R504D106 | Project: Data Engineering & Machine Learning | 5 | 5 | 5 | 5 | |||||||||||||||||||||
MLDERD24 |
RESEARCH AND DEVELOPMENT COMPETENCES
(Choose all) |
20 | ||||||||||||||||||||||||
R504D84 | Research Methods | 5 | 5 | 5 | 5 | |||||||||||||||||||||
AMKO001 | Planning Phase of the Bachelor´s Thesis | 5 | 5 | 5 | 5 | |||||||||||||||||||||
AMKO002 | Implementation Phase of the Bachelor´s Thesis | 5 | 5 | 5 | 5 | |||||||||||||||||||||
AMKO003 | Finishing Phase of the Bachelor´s Thesis | 5 | 5 | 5 | 5 | |||||||||||||||||||||
Total | 240 | 65 | 70 | 60 | 50 | 30 | 35 | 35 | 35 | 30 | 30 | 35 | 15 | 30 | 17.5 | 17.5 | 35 | 17.5 | 17.5 | 30 | 20 | 10 | 35 | 7.5 | 7.5 |
Due to the timing of optional and elective courses, credit accumulation per semester / academic year may vary.
Certificate structure
Bachelor of Engineering, Machine Learning and Data Engineering Competences (2022-)
Ethics
The graduating student adheres to the ethical principles and values of their field of profession, taking the principles of equality and non-discrimination into account. |
No attached course units |
ICT business
The graduating student knows the core concepts of business and entrepreneurship in the field of ICT, understands business operations and processes and how data can be utilized for creating business value. |
No attached course units |
Intelligent systems
The graduating student knows how to design and implement prototype-level sensor data acquisition systems and small-scale robots and how to program ML functionalities into them. |
No attached course units |
Internationality and multiculturalism
The graduating student is familiar with the principles of sustainable development, promotes their implementation and acts responsibly as a professional and a member of society. |
No attached course units |
Learning to learn
The graduating student recognises the strengths and development areas of their competence and learning methods, and they utilise the opportunities communities and digitalisation provide in their learning. |
No attached course units |
Machine learning (ML) and Artificial Intelligence (AI)
The graduating student understands broadly the areas of data analytics and machine learning (ML), is able to apply mathematical methods as well as programming in ML solution development, use existing ML services and develop own ML solutions and algorithms. |
No attached course units |
Mathematics and natural sciences
The graduating student thinks logically and mathematically, understands mathematics in engineering context and more specifically in machine learning and data engineering and is able to use mathematical methods in practice in own field. |
No attached course units |
Operating in a workplace
The graduating student has versatile working life skills and is able to operate in work communities of their field. |
No attached course units |
Proactive development
The graduating student is able to develop solutions that anticipate the future of their own field, applying existing knowledge and research and development methods. |
No attached course units |
Programming and software production
The graduating student knows how to apply programming languages and software production methods and tools required in ICT system development and ML. |
No attached course units |
Sustainable development
The graduating student is familiar with the principles of sustainable development, promotes their implementation and acts responsibly as a professional and a member of society. |
No attached course units |
Not grouped |
Start your UAS studies |
Seminar: Machine Learning and Data Engineering |
Algebra, Geometry and Trigonometry |
Probability, Statistics and Optimization |
Linear Algebra |
Electromagnetism |
Finnish 1 |
Finnish 2 |
Finnish 3 |
Finnish 4 |
Professional English for ICT Engineers 1 |
Professional English for ICT Engineers 2 |
Professional English for ICT Engineers 3 |
Swedish for ICT Engineers |
Swedish Oral Skills |
Swedish Written Language |
Business Skills and Entrepreneurship |
Data in Business Development |
Emerging Technologies |
Industrial Engineering and Management |
Project: Startup and Business Development |
Introduction to Programming |
Web Programming |
Introduction to Data Management |
Introduction to Data Analytics |
Introduction to Machine Learning Methods |
Algorithms and Data Structures |
Project: Data Analytics and Visualization |
ICT Infrastructure and Computer Technology |
Electronics |
Electronics in IoT |
Robotics |
Project: Internet of Things (IoT) |
Project: Robotics and Artificial Intelligence (AI) |
Basic Internship 1 |
Basic Internship 2 |
Professional Internship 1 |
Professional Internship 2 |
Advanced Professional Internship 1 |
Advanced Professional Internship 2 |
Advanced Data Analytics |
Advanced Machine Learning Methods |
Reinforcement Learning |
Project: Machine Learning and AI |
Cloud-Based Machine Learning |
Deep Learning |
Advanced Data Management |
Project: Data Engineering & Machine Learning |
Research Methods |
Planning Phase of the Bachelor´s Thesis |
Implementation Phase of the Bachelor´s Thesis |
Finishing Phase of the Bachelor´s Thesis |