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Machine Learning and Data Engineering

Degree:
Bachelor of Engineering

Degree title:
Bachelor of Engineering

Credits:
240 ects

Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), Rovaniemi, Autumn 2023
Code
(R54D23S)
Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), Rovaniemi, Autumn 2022
Code
(R54D22S)
Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), Rovaniemi, Autumn 2021
Code
(R54D21S)
Enrollment

13.03.2023 - 25.09.2023

Timing

01.09.2023 - 17.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Miika Aitomaa
Responsible person

Miika Aitomaa

Student groups
  • R54D23S
    Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023

Objective

The student learns fundamental mathematical concepts, principles, tools and terminology to be applied in professional studies.

Content

- Basics of logic and set theory
- Expressions, equations, inequality
- Functions
- Geometry; 2D, 3D
- Trigonometry

Location and time

Autumn term 2023, Lapland University of Applied Sciences, Rantavitikka campus (Rovaniemi, Jokiväylä 11)

Materials

Study material is available as an eBook and on the Moodle learning platform.

Teaching methods

Lessons and exercises

Exam schedules

The number and date of exams will be agreed on during the course. Resit is possible by the end of the next term.

Completion alternatives

Studying independently is possible. All exercises must be returned in time to be evaluated.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student knows the concepts of algebra, geometry and trigonometry, and is able to solve basic problems.

Assessment criteria, good (3)

The student understands the concepts of algebra, geometry and trigonometry, and is able to solve varied problems related to applications of algebra, geometry and trigonometry.

Assessment criteria, excellent (5)

The student understands the concepts of algebra, geometry and trigonometry, and is able to apply methods of algebra, geometry and trigonometry in solving and handling new types of problems.

Assessment methods and criteria

Evaluation is based on tests and/or exams and exercises. The emphasise on these will be agreed upon at the beginning of the course.

Assessment criteria, fail (0)

Student doesn't meet the basic requirements of grade 1.

Assessment criteria, satisfactory (1-2)

Student understands basic concepts of subject matter and is capable of solving basic exercises.

Assessment criteria, good (3-4)

Student understands more complicated concepts of subject matter and is capable of solving versatile exercises. Student uses correct mathematical language and can create logical solutions.

Assessment criteria, excellent (5)

Student is capable of applying concepts of subject matter to new problems and solve them in exact mathematical language.

Enrollment

02.10.2023 - 14.01.2024

Timing

15.01.2024 - 01.03.2024

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Ritva Lampela
Responsible person

Ritva Lampela

Student groups
  • R54D22S

Objective

The student can write various kinds of documents in the field of ICT using appropriate style and terminology and grammatically correct language. The student knows the principle rules of academic writing. The student knows the purchasing process of a technology company and the related business documents.

Proficiency level: CEFR B2-C1

Content

- Purchasing process of a technology company
- Business documents
- Academic writing
- Writing an abstract
- Writing a report
- Writing, speaking and listening exercises

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student knows the purchasing process of a technology company and the related business documents. The student knows the basics of academic writing and can draw up understandable documents using helping tools.

Assessment criteria, good (3)

The student knows the purchasing process of a technology company and the related business documents and appropriate terminology and phrases. The student knows the principle rules of academic writing and is able to prepare different kinds of documents using appropriate style.

Assessment criteria, excellent (5)

The student knows the purchasing process of a technology company and related documents. The student can draw up related documents using appropriate terminology and phrases and grammatically flawless language. The student knows and understands the rules of academic writing and is able to write grammatically correct academic texts.

Assessment criteria, satisfactory (1-2)

The student knows the purchasing process of a technology company and the related business documents. The student knows the basics of academic writing and can draw up understandable documents using helping tools.

Assessment criteria, good (3-4)

The student knows the purchasing process of a technology company and the related business documents and appropriate terminology and phrases. The student knows the principle rules of academic writing and is able to prepare different kinds of documents using appropriate style.

Assessment criteria, excellent (5)

The student knows the purchasing process of a technology company and related documents. The student can draw up related documents using appropriate terminology and phrases and grammatically flawless language. The student knows and understands the rules of academic writing and is able to write grammatically correct academic texts.

Enrollment

13.03.2023 - 24.09.2023

Timing

25.09.2023 - 10.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 35

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Juha Petäjäjärvi
Responsible person

Juha Petäjäjärvi

Student groups
  • R54D21S
    Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2021

Objective

The student is familiar with the features and key benefits of distributed systems. The student can create distributed applications in the cloud.

Content

- Common terminology, e.g. vertical vs. horizontal scaling, decentralized vs. distributed.
- The key features and benefits of distributed systems: fault tolerancy; low latency; scalability
- The categories of distributed systems and their common applications: distributed data stores; distributed computing; distributed file systems; distributed messaging systems; distributed ledgers and distributed applications

Teaching methods

Classroom teaching and excercises. Materials will be in Moodle.

Content scheduling

- Common terminology, e.g. vertical vs. horizontal scaling, decentralized vs. distributed.
- The key features and benefits of distributed systems: fault tolerancy; low latency; scalability
- The categories of distributed systems and their common applications: distributed data stores; distributed computing; distributed file systems; distributed messaging systems; distributed ledgers and distributed applications

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student is familar with the key features and benefits of distributed systems and some of their common applications.

Assessment criteria, good (3)

The student is familar with the key features and benefits of distributed systems and some of their common applications. The student knows how the distribution is used in some common applications to improve their latency, fault tolerancy and scalability.

Assessment criteria, excellent (5)

The student is familar with the key features and benefits of distributed systems and some of their common applications. The student can apply distribution to improve the latency, fault tolerancy and scalability of systems.

Assessment methods and criteria

This study unit is evaluated according to the student performance on assignments.

Assessment criteria, satisfactory (1-2)

The student is familar with the key features and benefits of distributed systems and some of their common applications.

Assessment criteria, good (3-4)

The student is familar with the key features and benefits of distributed systems and some of their common applications. The student knows how the distribution is used in some common applications to improve their latency, fault tolerancy and scalability.

Assessment criteria, excellent (5)

The student is familar with the key features and benefits of distributed systems and some of their common applications. The student can apply distribution to improve the latency, fault tolerancy and scalability of systems.

Enrollment

13.03.2023 - 27.08.2023

Timing

28.08.2023 - 17.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Miika Aitomaa
Responsible person

Miika Aitomaa

Student groups
  • R54D22S

Objective

The student learns fundamental mathematical concepts, principles, tools (including computing environments) and terminology for professional studies.

Content

- Mathematical objects: scalars, vectors, matrices and tensors
- Basic matrix operations
- Special type of matrices and vectors
- Systems of linear equations
- Determinants
- Analytic geometry; inner and outer products, projections
- Vector spaces and linear mappings
- Linear dependence, span
- Linear regression

Location and time

Autumn term 2023, Lapland University of Applied Sciences, Rantavitikka campus (Rovaniemi, Jokiväylä 11)

Materials

Study material is available as an eBook and on the Moodle learning platform.

Teaching methods

Lessons and exercises

Exam schedules

The number and date of exams will be agreed on during the course. Resit is possible by the end of the next term.

Completion alternatives

Studying independently is possible. All exercises must be returned in time to be evaluated.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student knows the concepts of linear algebra and is able to solve basic problems.

Assessment criteria, good (3)

The student understands the concepts of linear algebra and is able to solve varied problems related to applications of linear algebra.

Assessment criteria, excellent (5)

The student understands the concepts of linear algebra and is able to apply methods of linear algebra in solving and handling new types of problems.

Assessment methods and criteria

Evaluation is based on tests and/or exams, exercises, project. The emphasis on these will be agreed upon at the beginning of the course.

Assessment criteria, fail (0)

Student doesn't meet the basic requirements of grade 1.

Assessment criteria, satisfactory (1-2)

Student understands basic concepts of linear algebra and is capable of solving basic exercises.

Assessment criteria, good (3-4)

Student understands more complicated concepts of linear algebra and is capable of solving versatile exercises. Student uses correct mathematical language and can create logical solutions.

Assessment criteria, excellent (5)

Student is capable of applying concepts of linear algebra to new problems and solve them in exact mathematical language.

Enrollment

02.07.2023 - 31.07.2023

Timing

18.09.2023 - 15.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
  • Finnish
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Ritva Lampela
Responsible person

Ritva Lampela

Student groups
  • R54D23S
    Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023

Objective

Theme: communication in working life

The student is able to communicate interactively in different working life situations in the field of ICT. The student can prepare and give a professional presentation, make phone calls and use e-mail in an appropriate and professional manner. The student can participate meetings and negotiations and write related documents. The student can tell about their education and discuss topics in the field of ICT. The student can use appropriate style in different situations and consider cultural differences in communication.

Proficiency level B2

Content

Telephone and e-mail communication
Professional presentation and presentation skills
Work of an ICT engineer
Applying for a job, CV, job interview
Meeting terminology
Professional language, terminology and texts
Speaking, writing and listening tasks

Location and time

Autumn term 2023, Rovaniemi

Materials

Study material available on Moodle.

Teaching methods

Contact lessons, independent studying

Exam schedules

Exam dates will be decided during the course.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student communicates understandably in English in different working life situations both in writing and speech. The student is able to apply for work using English and knows the language of formal meetings, as well as ICT related terminology.

Assessment criteria, good (3)

The student is able to communicate fairly fluently in English both in speech and in writing and is able to use appropriate style in different situations. The student knows rather advanced terminology of working life and information and communication technology. The student is able to represent their employer in various situations.

Assessment criteria, excellent (5)

The student communicates fluently both in speech and in writing in English using appropriate style. The student uses language structures flawlessly and pronounces fluently and clearly. The student can use correct terminology in various working life situations and masters the terminology of information and communication technology. The student is able to take into account multicultural aspects in communication.

Assessment methods and criteria

Assessment is based on the student's active participation on contact lessons. The required assignments must be done and there is also a written exam. Oral skills will be assessed continuously during the study unit.
The assessment is based on the Common European framework, skill level B2.

Assessment criteria, satisfactory (1-2)

The student can represent his or her company and present products and work in projects if given enough time and tools to prepare beforehand. The student can use the basic terminology of ICT, uses the basic structures of English mainly correctly and pronounces English understandably.

Assessment criteria, good (3-4)

The student is able to represent own company and products fluently in English using appropriate style. The student uses versatile language structures and vocabulary and pronounces fluently and clearly. The student can use appropriate styles in multicultural environments without the language skills restraining communication

Assessment criteria, excellent (5)

The student can represent own company and present products very fluently and accurately using appropriate style. The student uses language structures flawlessly and pronounces fluently and clearly. The student can use appropriate styles in multicultural environments without the language skills restraining communication.

Enrollment

13.03.2023 - 24.09.2023

Timing

25.09.2023 - 22.12.2023

Credits

5 op

Virtual proportion (cr)

1 op

Mode of delivery

80 % Contact teaching, 20 % Distance learning

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Maisa Mielikäinen
Responsible person

Maisa Mielikäinen

Student groups
  • R54D22S

Objective

The student knows how to implement an IoT full-stack path, starting with the sensors and ending with the user interface. The student knows how to use the most commonly used IoT platforms, protocols and tools. The student knows how to implement an IoT full-stack according to the given assignment.

The student knows the special features of agile methods and knows how to apply them in a practical IoT development project. The student understands the special features of agile system development and knows how to use the tools suitable for it. The student understands the perspectives of sustainable development in IoT system development and understands the importance of ethical and responsible operations in multicultural communities.

Content

- IoT full-stack open assignment solution
- Agile project management and DevOps philosophy
- Sustainable development and ethical principles in a multicultural community

Location and time

The theoretical processing of the topic is mainly carried out before the fall vacation (week 42). The practical project work will start after the fall vacation. The project follows the principles of multi-location work, sometimes working on campus and sometimes online.

Materials

Literature and videos related to agile methods can be found on the Internet. Students should especially familiarize themselves with the SCRUM methodology from the perspective of project management. The document templates are offered in connection with the course in Moodle.

Teaching methods

Students are formed into project teams who implement a IoT practical project using agile project management methods (SCRUM). Learning includes lectures and project workshops around the topic, reviews and guidance sessions. The outputs of the projects will be presented at the joint project exhibition day of all ICT engineering students' projects at the end of the semester.

The study course is integrated with other study courses of the semester.

Completion alternatives

The course can also be completed in a working life project. The method of implementation will be separately agreed in detail with the responsible teacher.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student is able to work in an agile project where an IoT system is produced, but the responsibility and participation is insufficient. The activity is passive and there is a lack of initiative. Insufficient participation or inactivity will lead to the rejection of the course. The student follows the chosen project process for agile system development randomly or with strong guidance.

Assessment criteria, good (3)

The student is able to work constructively in a team, to make technical and process-related proposals for the assignment in order to solve the given problem. The student's attitude and operation are active. The student considers other members of the community ethically and culturally respect. The student follows the project process chosen for agile system development systematically and mostly independently. The solution for the assignment produced by the project meets the requirements. The final result has been reached to some extent guided, but mostly independently.

Assessment criteria, excellent (5)

The student turns out to be a key person in the project team. The student is able to constructively and versatilely make technical and process-related proposals for assignments. The student clearly bears responsibility for the success of the entire project as well as the tasks and well-being of the other team members. The project process is independent and high-quality. The project produced a high-quality solution for the technical assignment. The final result has been reached mainly independently, relying on guidance in a professional and expert manner.

Assessment methods and criteria

The evaluation is based on the student's activity, responsibility and results as an individual (50%) and at the project team level (50%). The evaluation is continuous, which means that the entire process, not just the end result, is the subject of the evaluation.

Enrollment

02.10.2023 - 07.01.2024

Timing

29.01.2024 - 31.05.2024

Credits

5 op

Virtual proportion (cr)

3 op

Mode of delivery

40 % Contact teaching, 60 % Distance learning

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • Finnish
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Päivi Saari
Responsible person

Päivi Saari

Student groups
  • R54D23S
    Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023

Objective

The student is able to manage in working life situations in Swedish using appropriate style. The student can tell about his or her background, (future) job, education, participate in discussions, participate in an informal meeting, write a job application and participate in a job interview. The student can follow and collect information in ICT branch and is able to present ICT companies and ICT products.

Proficiency level CEFR B1-B2

Content

- Background, education, ICT jobs
- Professional terminology of ICT
- ICT companies and products
- Applying a job
- Participating in an informal meeting
- Written and oral assignments and listening tasks

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student can tell about his or her background, education, (future) job, present a company and products if given enough time and tools to prepare beforehand. The student is able to use the basic terminology of ICT, uses the basic structures of Swedish mainly correctly.

Assessment criteria, good (3)

The student is able to tell about his or her background, education, (future) job and present company and products fluently in Swedish using appropriate style. The student uses versatile language structures and vocabulary. The student can use appropriate styles without the language skills restraining communication.

Assessment criteria, excellent (5)

The student tells his or her background, education and (future job) very fluently. The student can present ICT companies and products very fluently and accurately using appropriate style. The student uses language structures and pronounces fluently and clearly. The student can use appropriate styles without the language skills restraining communication.

Enrollment

13.03.2023 - 03.09.2023

Timing

04.09.2023 - 31.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
  • Finnish
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
  • Degree Programme in Information and Communication Technology
Teachers
  • Ari Karjalainen
  • Anssi Ylinampa
Responsible person

Ari Karjalainen

Student groups
  • R54D22S

Objective

The student knows the components of circuits and is able to analyze and calculate the currents and voltages of simple DC and AC circuits. He understands the meaning and purpose of different components in circuits. The student is able to form appropriate circuits for electronic connections and to simulate connections with a computer-based circuit simulator program.

Content

- Electromagnetic induction
- Power
- Voltage
- Circuits
- Circuit components (voltage sources, resistors, capacitors and inductors)
- RLC circuits, simple filters
- Alternating current
- Circuit simulation and simulation tools

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student is familiar with the basics of circuits and related laws such as Ohm's law and Kirchfoff's laws. The student understands the purpose of circuit components and is able to solve simple circuits with a simulation tool.

Assessment criteria, good (3)

The student is familiar with the basics of circuits and related laws such as Ohm's law and Kirchfoff's laws.
The student understands the purpose of circuit components and is able to solve complex circuits with a simulation tool and simple circuits with mathematical methods based on Ohm's and Kirchoff's laws.
The student knows the structures of basic filters and is able to simulate their operation with a simulation tool,

Assessment criteria, excellent (5)

The student will understand and apply the basics of circuits and related laws such as Ohm’s Law and Kirchfoff’s Laws. The student understands the purpose of circuit components and is able to solve complex circuits with a simulation tool and mathematical methods based on Ohm's and Kirchoff's laws.
The student knows and understands the structures and operation of basic filters and is able to design and simulate filters that meet the set requirements with a simulation tool.

Enrollment

02.12.2023 - 31.12.2023

Timing

29.01.2024 - 28.04.2024

Credits

5 op

Mode of delivery

Contact teaching

Teaching languages
  • English
Seats

0 - 30

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Miika Aitomaa
  • Jouko Teeriaho
Responsible person

Jouko Teeriaho

Student groups
  • R54D23S
    Bachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023

Objective

The student learns fundamental mathematical concepts, principles, tools (including computing environments) and terminology of statistics, probability and optimization for professional studies.

Content

Theory of statistics and probability
- Numerical and graphical description of data
- Probability, probability rules and theorems, probability distribution
- Modeling, parameter estimation
- Model selection, decision theory
- Analysis task types

Optimization, differential calculus and numerical computation
- Objective function
- Critical points, extrema
- Types of optimization problems
- Limit, derivative, partial derivative, differentiation rules
- Iterative gradient-based optimization methods, derivative-free methods

Location and time

Spring term 2024, Lapland University of Applied Sciences, Rantavitikka campus (Rovaniemi, Jokiväylä 11)

Materials

Study material is available as an eBook and on the Moodle learning platform.

Teaching methods

Lessons and exercises

Exam schedules

The number and date of exams will be agreed on during the course. Resit is possible by the end of the next term.

Completion alternatives

Studying independently is possible. All exercises must be returned in time to be evaluated.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

Assessment criteria - grade 1
The student knows the concepts of probability, statistics, and optimization and is able to solve basic problems.

Assessment criteria, good (3)

Assessment criteria - grade 3
The student understands the concepts of probability, statistics, and optimization and is able to solve varied problems related to applications of probability, statistics, and optimization.

Assessment criteria, excellent (5)

The student understands the concepts of probability, statistics, and optimization and is able to apply methods of probability, statistics, and optimization in solving and handling new types of problems.

Assessment methods and criteria

Evaluation is based on exercises (homework) and/or exams. The emphasis on these will be agreed upon at the beginning of the course.

Assessment criteria, fail (0)

Student doesn't meet the basic requirements of grade 1.

Assessment criteria, satisfactory (1-2)

Student knows the basic concepts of probability, statistics, and optimization, and is able to solve basic problems.

Assessment criteria, good (3-4)

Student understands more complicated concepts of probability, statistics, and optimization, and is capable of solving versatile exercises. Student uses correct mathematical language and can create logical solutions.

Assessment criteria, excellent (5)

Student is capable of applying concepts of probability, statistics, and optimization to new problems and solve them in exact mathematical language.

Enrollment

13.03.2023 - 30.09.2023

Timing

01.09.2023 - 31.12.2023

Credits

5 op

Mode of delivery

Contact teaching

Unit

Bachelor of Engineering, Information Technology

Teaching languages
  • English
Seats

0 - 20

Degree programmes
  • Machine Learning and Data Engineering
Teachers
  • Heidi Huhta
Responsible person

Heidi Huhta

Student groups
  • R54D22S

Objective

The student is able to communicate interactively in different working life situations in the field of ICT. The student can prepare and give a professional presentation, make business phone calls, use e-mail in an appropriate and professional manner. The student can participate meetings and handle negotiations, write memos and summaries. The student can use appropriate style in different situations and considers cultural differences in communication.

Proficiency level: CEFR B2 - C1

Content

- Telephone and e-mail communication
- Professional presentation and presentation skills
- Applying for a job, CV, job interview
- Working life terminology
- Meeting terminology
- Negotiations
- Speaking, writing and listening tasks

Materials

Material on Moodle

Teaching methods

Contact lessons, individual and team tasks, listening and speaking tasks

Exam schedules

Will be agreed during the course.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student communicates understandably in English about the topics of working life and information and communication technology. The student knows the basic terminology.

Assessment criteria, good (3)

The student is able to communicate fairly fluently in English both in speech and in writing. The student knows rather advanced terminology of working life and information and communication technology.

Assessment criteria, excellent (5)

The student communicates very fluently both in speech and writing In English. The student uses the terminology of working life and information and communication technology skillfully and is able to take into account multicultural aspects in communication.

Assessment methods and criteria

Assessment is based on student's active participation, doing assignments, oral presentations and written exam.

The course is on level B2

Assessment criteria, fail (0)

The student does not reach minimum requirements.

Assessment criteria, satisfactory (1-2)

Satisfactory 1

Is able to communicate in working life situations to some extent, but language skills are limited.

Satisfactory 2
Communication is mainly understandable and the student understands different styles to some extent. Can use professional terminology, can communicate in multicultural environment, but language skills form limitations.

Assessment criteria, good (3-4)

Good 3
Can communicate in multicultural environment fairly well and uses professional terminology.

Good 4
Can communicate well, understands different styles and has versatile vocabulary.

Assessment criteria, excellent (5)

Laudable 5

Communication is accurate, efficient and fluent utilizing different styles. Can use specialized terminology and has wide vocabulary.
Excellent language skills in speech and writing.