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Blekinge Institute of Technology
Department of Mechanical Engineering
Revision: 1
Reg.no:
Course syllabus
Applied Machine Learning
Applied Machine Learning
7.5 credits (7,5 högskolepoäng)
Course code: MT1588
Main field of study: Mechanical Engineering
Disciplinary domain: Technology
Education level: First-cycle
Specialization: G1F - First cycle, has less than 60 credits in first-cycle course/s as entry requirements
Language of instruction: English
Applies from: 2025-09-09
Approved: 2025-09-09
1. Descision
This course is established by Dean 2024-10-30. The course syllabus is approved by Head of Department of Mechanical Engineering 2025-09-09 and applies from 2025-09-09.
2. Entry requirements
Admission to the course requires 5 completed credits in linear algebra, 5 completed credits in programming, and 5 completed credits in mathematical statistics. English 6.
3. Objective and content
3.1 Objective
The objective of this course is to introduce the practical application of machine learning (ML) techniques in solving engineering problems. It bridges the gap between theoretical understanding and real-world implementation by integrating hands-on exercises with case studies from industry. Students will learn to critically apply ML models to engineering datasets, evaluate model performance, and interpret the results in the context of engineering systems.
3.2 Content
Topics covered include:
- Introduction to Machine Learning
- Data Handling and Preprocessing
- Supervised Learning
- Unsupervised Learning
- Model Evaluation and Selection
- Applied Case Studies in Mechanical Engineering and/or Electrical Engineering, for example
- Ethics and Interpretability in Machine Learning
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:
- Describe key machine learning concepts, algorithms, and workflows.
- Identify and explain the potential and limitations of machine learning in industrial engineering applications.
4.2. Competence and skills
On completion of the course, the student will be able to:
- Preprocess and analyze engineering data using appropriate machine learning tools.
- Train, tune, and evaluate machine learning models on real-world datasets.
- Implement and document ML-based solutions for engineering problems using programming languages and machine learning software.
- Design and present a complete ML pipeline for a practical case.
4.3. Judgement and approach
On completion of the course, the student will be able to:
- Critically assess which ML models are more suitable for specific engineering tasks.
- Reflect on the reliability and interpretability of data-driven models in engineering decision-making.
- Assess the ethical implications that may arise from integrating machine learning into industrial engineering practice.
5. Learning activities
The course will combine classroom lectures, coding tutorials, group teamwork, and supervising activities, and project work on application of machine learning.
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2605 | Written Assignment 1 | 3 credits | GU |
| 2615 | Written Assignment 2 | 3 credits | GU |
| 2625 | Project Assignment | 1.5 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
The course does not have a reference book. Course literature will be distributed by teachers during the course in the form of teaching material and scientific publications.