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Blekinge Institute of Technology
Department of Mathematics and Natural Science

Revision: 2
Reg.no: BTH-4.1.14-0039-2025


Course syllabus

Introductory Course in Machine Learning, Sensors and Systems

Introductory Course in Machine Learning, Sensors and Systems

3 credits (3 högskolepoäng)

Course code: ET1562
Main field of study: Electrical Engineering
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G2F - First cycle, has at least 60 credits in first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2025-03-12
Approved: 2025-02-21

1. Descision

This course is established by Dean 2024-06-20. The course syllabus is approved by Head of Department of Mathematics and Natural Science 2025-02-21 and applies from 2025-03-12.

2. Entry requirements

Admission to the course requires a Bachelor of Science or Bachelor of Science in Engineering (180 credits) in science, technology, engineering, mathematics or equivalent. English 6.

3. Objective and content

3.1 Objective

The aim of this course is to introduce the programme, including the scope, goals, educational platforms, and tools to the students. They also will be introduced to research projects and capabilities at the university. In addition, the course provides fundamental knowledge and practical skills in sensor systems, signal processing, machine learning, and computational problem-solving using modern tools like MATLAB, Python and related libraries. The students will also have the opportunity to practice core concepts in signal processing, machine learning, and data analysis through a real-world application. Furthermore, the course emphasizes communication skills and scientific writing in order to convey technical concepts clearly and concisely in written reports.

3.2 Content

· Introduction to core concepts of signal processing and machine learning.
· Programming techniques in MATLAB and Python.
· Technical report writing and communication skills.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • explain foundational concepts in signal processing and machine learning.
  • describe the role of programming tools like MATLAB and Python in simulating systems, analyzing data, and developing algorithms.
  • describe the importance of signal processing and machine learning to address technological challenges.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • utilize the educational platform and tools for their studies.
  • apply basic signal processing and machine learning algorithms to model simple systems and solve introductory engineering problems using Python/MATLAB.
  • develop and execute code for basic signal analysis and computational simulations.
  • communicate technical concepts clearly in structured written reports.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • critically assess the strengths and limitations of signal processing and machine learning methods in specific contexts.
  • evaluate the ethical implications of algorithmic decisions.
  • determine the appropriateness of a method for a given engineering problem.
  • reflect on the societal impact of machine learning-driven solutions in applications.

5. Learning activities

Teaching consists of lectures, exercises and project. The students present their work in writing in the project reports.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2510 Project 3 credits GU

The course will be graded G Pass, Ux Failed result, a little more work required, U Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Study materials will be provided to the students during the course.