Machine Learning and Data-Driven Methods for Mechanical Engineering, 6 credits
Maskininlärning och datadrivna metoder för maskintekniska tillämpningar, 6 hp
TMMV64
Main field of study
Mechanical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Saeed SalehiDirector of studies or equivalent
Johan RennerEducation components
Preliminary scheduled hours: 100 hRecommended self-study hours: 60 h
| Course offered for | Semester | Period | Timetable module | Language | Campus | ECV | |
|---|---|---|---|---|---|---|---|
| 6MAER | Aeronautical Engineering, Master's Programme | 3 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6CEMM | Energy - Environment - Management, Master of Science in Engineering | 9 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6CEMM | Energy - Environment - Management, Master of Science in Engineering (Technology for Sustainable Development) | 9 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6CMMM | Mechanical Engineering, Master of Science in Engineering | 9 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6CMMM | Mechanical Engineering, Master of Science in Engineering (Engineering Mechanics) | 9 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6MMEC | Mechanical Engineering, Master's Programme | 3 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
| 6MMEC | Mechanical Engineering, Master's Programme (Applied Mechanics) | 3 (Autumn 2026) | 2 | 1 | English | Linköping, Valla | E |
Main field of study
Mechanical EngineeringCourse level
Second cycleAdvancement level
A1NCourse offered for
- Master of Science in Energy - Environment - Management
- Master of Science in Mechanical Engineering
- Master's Programme in Mechanical Engineering
- Master's Programme in Aeronautical Engineering
Prerequisites
Basic courses in mathematics, linear algebra, programming, computational mechanics. Fluid Mechanics and Heat Transfer
Intended learning outcomes
After completing the course, the student will be able to
- Explain fundamental concepts in data-driven and machine learning
- Process, analyze, and visualize engineering datasets using appropriate computational tools
- Apply data-driven methods, including reduced-order modelling, to extract and represent relevant information from data
- Implement and evaluate machine-learning models for different types of engineering problems
- Apply, assess, and justify the choice, performance, and limitations of data-driven and machine-learning methods for a given mechanical engineering problem
Course content
The course covers fundamental principles of data-driven methods and machine learning for mechanical engineering applications. Topics include data representation and structuring, data preprocessing and visualization, dimensionality reduction and feature extraction, and data-driven reduced-order modeling. The course further addresses machine learning methods, neural networks, and deep learning techniques, and their application to engineering problems.
Teaching and working methods
The course is delivered through a combination of lectures, computer laboratory sessions, assignments, and a final project. Lectures introduce fundamental concepts and methods, while laboratory sessions and assignments focus on practical implementation and application using Python programming. The final project provides an opportunity for in-depth application of the course methods to an engineering problem and promotes independent problem solving and critical analysis.
Examination
| UPG1 | Assignments | 6 credits | U, 3, 4, 5 |
Examination is based on assignments and a final project. The assignments are carried out throughout the course. The final project is conducted in small groups, focusing on applying the course methods to engineering problems, and is examined through a written report. The examination may also include oral components. The final grade is determined through a summative assessment of all examination components.
All mandatory components of the course must be completed to pass the course. Additional optional tasks may be offered to support higher grades.
Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
About teaching and examination language
The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows:
- If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly in English. Examination language is Swedish, but parts of the examination can be in English.
- If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
- If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.
Other
The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.
The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.
The course is campus-based at the location specified for the course, unless otherwise stated under “Teaching and working methods”. Please note, in a campus-based course occasional remote sessions could be included.
Department
Institutionen för ekonomisk och industriell utvecklingCourse literature
Books
- Steven L Brunton, J Nathan Kutz, (2022) Data-Driven Science and Engineering, Machine Learning, Dynamical Systems, and Control 2 Cambridge University Press
ISBN: 9781009098489
https://www.cambridge.org/highereducation/books/data-driven-science-and-engineering/6F9A730B7A9A9F43F68CF21A24BEC339#contents
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| UPG1 | Assignments | 6 credits | U, 3, 4, 5 |
Examination is based on assignments and a final project. The assignments are carried out throughout the course. The final project is conducted in small groups, focusing on applying the course methods to engineering problems, and is examined through a written report. The examination may also include oral components. The final grade is determined through a summative assessment of all examination components.
All mandatory components of the course must be completed to pass the course. Additional optional tasks may be offered to support higher grades.
Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.
Course syllabus
A syllabus must be established for each course. The syllabus specifies the aim and contents of the course, and the prior knowledge that a student must have in order to be able to benefit from the course.
Timetabling
Program courses are timetabled after a decision has been made for this course concerning its assignment to a timetable module. Single subject courses can be timetabled at other times.
Interruption in and deregistration from a course
The LiU decision, Guidelines concerning confirmation of participation in education, Dnr LiU-2020-02256 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/764582), states that interruptions in study are to be recorded in Ladok. Thus, all students who do not participate in a course for which they have registered are therefore obliged to report the interruption so that this can be noted in Ladok. Deregistration from or interrupting a course is carried out using a Web-based form.
Cancelled courses and changes to the course syllabus
Courses with few participants (fewer than 10) may be cancelled or organised in a manner that differs from that stated in the course syllabus. The Dean is to deliberate and decide whether a course is to be cancelled or changed from the course syllabus. For single subject courses, the cancellation must be done before students are admitted to the course (in accordance with LiUs regulation Dnr LiU-2022-01200, https://styrdokument.liu.se/Regelsamling/VisaBeslut/622645).
Guidelines relating to examinations and examiners
For details, see Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379 (http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592).
An examiner must be employed as a teacher at LiU according to the LiU Regulations for Appointments, Dnr LiU-2022-04445 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/622784). For courses in second-cycle, the following teachers can be appointed as examiner: Professor (including Adjunct and Visiting Professor), Associate Professor (including Adjunct), Senior Lecturer (including Adjunct and Visiting Senior Lecturer), Research Fellow, or Postdoc. For courses in first-cycle, Assistant Lecturer (including Adjunct and Visiting Assistant Lecturer) can also be appointed as examiner in addition to those listed for second-cycle courses. In exceptional cases, a Part-time Lecturer can also be appointed as an examiner at both first- and second cycle, see Delegation of authority for the Board of Faculty of Science and Engineering.
Forms of examination
Principles for examination
Written and oral examinations and digital and computer-based examinations are held at least three times a year: once immediately after the end of the course, once in August, and once (usually) in one of the re-examination periods. Examinations held at other times are to follow a decision of the faculty programme board.
Principles for examination scheduling for courses that follow the study periods:
- courses given in VT1 are examined for the first time in March, with re-examination in June and August
- courses given in VT2 are examined for the first time in May, with re-examination in August and January
- courses given in HT1 are examined for the first time in October, with re-examination in January and August
- courses given in HT2 are examined for the first time in January, with re-examination in March and in August.
The examination schedule is based on the structure of timetable modules, but there may be deviations from this, mainly in the case of courses that are studied and examined for several programmes and in lower grades (i.e. 1 and 2).
Examinations for courses that the faculty programme board has decided are to be held in alternate years are held three times during the school year in which the course is given according to the principles stated above.
Examinations for courses that are cancelled or rescheduled such that they are not given in one or several years are held three times during the year that immediately follows the course, with examination scheduling that corresponds to the scheduling that was in force before the course was cancelled or rescheduled.
When a course, or a written or oral examination (TEN, DIT, DAT, MUN), is given for the last time, the regular examination and two re-examinations will be offered. Thereafter, examinations are phased out by offering three examinations during the following academic year at the same times as the examinations in any substitute course. The exception is courses given in the period HT1, where the three examination occasions are January, March and August. If there is no substitute course, three examinations will be offered during re-examination periods during the following academic year. Other examination times are decided by the faculty programme board. In all cases above, the examination is also offered one more time during the academic year after the following, unless the faculty programme board decides otherwise. In total, 6 re-examinations are offered, of which 2 are regular re-examinations. In the examination registration system, the examinations given for the penultimate time and the last time are denoted.
If a course is given during several periods of the year (for programmes, or on different occasions for different programmes) the faculty programme board or boards determine together the scheduling and frequency of re-examination occasions.
For single subject courses, written and oral examinations can be held at other times.
Retakes of other forms of examination
Regulations concerning retakes of other forms of examination than written examinations and digital and computer-based examinations are given in the LiU guidelines for examinations and examiners, Dnr LiU-2023-00379 (http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592).
In principle, other examination forms should be handled in the same way as a written examination when they are given for the last time. However, the times for the examination may vary based on the nature of the element compared to the times for the written examinations.
Course closure
For Decision on Routines for Administration of the Discontinuation of Educational Programs, Freestanding Courses and Courses in Programs, see Dnr LiU-2021-04782 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/1156410). After a decision on closure and after the end of the discontinuation period, the students are referred to a replacement course (or similar) according to information in the course syllabus or programme syllabus. If a student has passed some part/parts of a closed program course but not all, and there is an at least partially replacing course, an assessment of crediting can be made. For questions about the crediting of course components, contact the Study councellors.
Registration for examination
In order to take an written, digital or computer-based examination, registration in advance is mandatory, see decision in the university’s rule book Dnr LiU-2020-04559 (https://styrdokument.liu.se/Regelsamling/VisaBeslut/622682). An unregistered student can thus not be offered a place. The registration is done by the student at the Student Portal or in the LiU-app during the registration period. The registration period opens 30 days before the date of the examination and closes 10 days before the date of the examination. Candidates are informed of the location of the examination by email, four days in advance.
Code of conduct for students during examinations
Details are given in a decision in the university’s rule book, Dnr LiU-2020-04559 (http://styrdokument.liu.se/Regelsamling/VisaBeslut/622682).
Retakes for higher grade
Students at the Faculty of Science and Engineering at LiU have the right to retake written examinations and digital and computer-based examinations in an attempt to achieve a higher grade. This is valid for all examination components with code “TEN”, “DIT” and "DAT". The same right may not be exercised for other examination components, unless otherwise specified in the course syllabus.
A retake is not possible on courses that are included in an issued degree diploma.
Grades
The grades that are preferably to be used are Fail (U), Pass (3), Pass not without distinction (4) and Pass with distinction (5).
- Grades U, 3, 4, 5 are to be awarded for courses that have written or digital examinations.
- Grades Fail (U) and Pass (G) may be awarded for courses with a large degree of practical components such as laboratory work, project work and group work.
- Grades Fail (U) and Pass (G) are to be used for degree projects and other independent work.
Examination components
The following examination components and associated module codes are used at the Faculty of Science and Engineering:
- Grades U, 3, 4, 5 are to be awarded for written examinations (TEN) and digital examinations (DIT).
- Examination components for which the grades Fail (U) and Pass (G) may be awarded are laboratory work (LAB), project work (PRA), preparatory written examination (KTR), digital preparatory written examination (DIK), oral examination (MUN), computer-based examination in a computer lab (DAT), digital preparatory written examination in a computer lab (DAK), home assignment (HEM), and assignment (UPG).
- Students receive grades either Fail (U) or Pass (G) for other examination components in which the examination criteria are satisfied principally through active attendance such as tutorial group (BAS) or examination item (MOM).
- Grades Fail (U) and Pass (G) are to be used for the examination components Opposition (OPPO) and Attendance at thesis presentation (AUSK) (i.e. part of the degree project).
In general, the following applies:
- Mandatory course components must be scored and given a module code.
- Examination components that are not scored, cannot be mandatory. Hence, it is voluntary to participate in these examinations, and the voluntariness must be clearly stated. Additionally, if there are any associated conditions to the examination component, these must be clearly stated as well.
- For courses with more than one examination component with grades U,3,4,5, it shall be clearly stated how the final grade is weighted.
For mandatory components, the following applies (in accordance with the LiU Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379 http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592):
- If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.
For possibilities to alternative forms of examinations, the following applies (in accordance with the LiU Guidelines for education and examination for first-cycle and second-cycle education at Linköping University, Dnr LiU-2023-00379 http://styrdokument.liu.se/Regelsamling/VisaBeslut/917592):
- If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.
- If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.
- An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaing the objectives of the course.
Reporting of examination results
The examination results for a student are reported at the relevant department.
Plagiarism
For examinations that involve the writing of reports, in cases in which it can be assumed that the student has had access to other sources (such as during project work, writing essays, etc.), the material submitted must be prepared in accordance with principles for acceptable practice when referring to sources when the text, images, ideas, data, etc. of other people are used. This is done by using references or quotations for which the source is specified. It is also to be made clear whether the author has reused his or her own text, images, ideas, data, etc. from previous examinations, such as degree projects, project reports, etc. (this is sometimes known as “self-plagiarism”).
A failure to specify such sources may be regarded as attempted deception during examination.
Attempts to cheat
In the event of a suspected attempt by a student to cheat during an examination, or when study performance is to be assessed as specified in Chapter 10 of the Higher Education Ordinance, the examiner is to report this to the disciplinary board of the university. Possible consequences for the student are suspension from study and a formal warning. More information is available at Cheating, deception and plagiarism.
Linköping University has also produced a guide for teachers and students' use of generative AI in education (Dnr LiU-2023-02660). As a student, you are always expected to gain knowledge of what applies to each course (including the degree project). In general, clarity to where and how generative AI has been used is important.
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at https://styrdokument.liu.se/Regelsamling/Innehall.
Books
ISBN: 9781009098489
Note: The course matrix might contain more information in Swedish.
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (courses on G1X-level) |
|
|
X
|
Basic knowledge in mathematics and linear algebra is assumed |
||
| 1.2 Fundamental engineering knowledge (courses on G1X-level) |
|
|
X
|
Basic engineering knowledge is assumed and used in the assignments and the project |
||
| 1.3 Further knowledge, methods and tools in any of : mathematics, natural sciences, engineering (courses at G2X level) |
|
X
|
|
UPG1
|
Machine learning and data-driven methods for engineering applications are introduced, applied, and examined through assignments and project |
|
| 1.4 Advanced knowledge, methods and tools in any of: mathematics, natural sciences, engineering (courses at A1X level) |
|
X
|
|
UPG1
|
Selected advanced topics are introduced and applied in the assignments and the project |
|
| 1.5 Insight into current research and development work |
X
|
|
|
UPG1
|
Guest lectures from academia and industry |
|
| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
|
UPG1
|
Analytical thinking and problem-solving skills are developed through formulation, implementation, and evaluation of data-driven and machine learning models in the indivual assignments and the project |
|
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
|
UPG1
|
An experimental and investigative approach is developed through systematic testing, comparison, and evaluation of the trained data-driven and machine learning models |
|
| 2.3 System thinking |
|
X
|
|
UPG1
|
System thinking is developed by relating data-driven and machine learning models to engineering systems and application contexts |
|
| 2.4 Attitudes, thought, and learning |
|
X
|
|
UPG1
|
An independent and reflective approach to learning is developed through iterative model development, evaluation, and adaptation in engineering contexts |
|
| 2.5 Ethics, equity, and other responsibilities |
X
|
|
|
Ethical aspects and responsible use of data-driven and machine learning methods in engineering applications are introduced |
||
| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
|
X
|
UPG1
|
Project work in group |
|
| 3.2 Communications |
|
X
|
X
|
UPG1
|
Scientific communication through visualization and written and oral presentation and discussion of the work. |
|
| 3.3 Communication in foreign languages |
|
|
X
|
UPG1
|
English is used in written and oral communication in the course. |
|
| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 Societal conditions, including economically, socially and ecologically sustainable development |
X
|
|
|
Societal and sustainability aspects related to the use of data-driven and machine learning methods in engineering |
||
| 4.2 Enterprise and business context |
|
|
|
|||
| 4.3 Conceiving, system engineering and management |
|
X
|
|
UPG1
|
Identification of suitable engineering problems for data-driven and machine learning approaches |
|
| 4.4 Designing |
|
X
|
|
UPG1
|
Application of data-driven and machine learning methods to engineering problems |
|
| 4.5 Implementing |
|
|
|
|||
| 4.6 Operating |
|
|
|
|||
| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economically, socially and ecologically sustainable development within research or development projects |
X
|
|
|
Societal and sustainability aspects related to data-driven and machine learning projects are introduced |
||
| 5.2 Economic conditions for research or development projects |
|
|
|
|||
| 5.3 Identification of needs, structuring and planning of research or development projects |
|
X
|
|
UPG1
|
Research or development tasks are formulated and planned as part of the project work |
|
| 5.4 Execution of research or development projects |
|
X
|
|
UPG1
|
A data-driven or machine learning project is carried out based on a defined engineering problem |
|
| 5.5 Presentation and evaluation of research or development projects |
|
X
|
|
UPG1
|
Project work is reported and evaluated through written documentation and analysis of results |
|
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