Course - Machine Learning for Engineers - TMM4128
TMM4128 - Machine Learning for Engineers
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| School exam | 60/100 | 4 hours | D | |
| Portfolio | 40/100 |
Course content
Machine learning (ML) is a branch of AI that focuses on learning from data to design automated systems that can improve their performance with experience. In recent years, machine learning has been used in a wide range of engineering applications, including: autonomous cars, predicting mechanical failure, quality assessment, robotic vision and intelligent control among others.
This course provides a thorough introduction to machine learning and hands-on experience with its practical applications. The topics taught in this course will cover fundamental principles in machine learning as well as the theoretical bases for its algorithms and how they can be optimally applied.
Learning outcome
Having successfully completed this course student will be able to acquire the following:
Knowledge:
- Learn the fundamental principles of supervised, unsupervised and reinforcement learning.
- Acquiring knowledge of using ML to solve practical problems relevant for engineers.
Skills:
- Apply data handling, feature engineering and data pre-processing techniques
- Gain experience to systematically work with data to learn new patterns.
General competence:
- At the end of this course students will understand the strengths and limitations of well-known machine learning methods, and learn how to analyse data to identify trends.
Learning methods and activities
Learning activities in this course include: lectures, preparing seminars, working on a mini project, and contribution to discussion.
The examination papers will be given in English only, also it is expected that English is used for answering the exam.
Further on evaluation
Portfolio assessment and final exam are the bases of the course grade.The grade is divided into 40% for portfolio assessment and 60% for final exams. The portfolio is marked in %-scores and it includes oral presentations and a project that involves the application of a machine learning model.
The portfolio assessment includes: 10% dedicated to seminars, and 30% dedicated to a project. Both revolve around choosing a data set and applying machine learning models in real world applications.
The seminars will include a short seminar that introduces the chosen data set for the project and a longer seminar that explains the pace of work after the project midterm report has been submitted.
In these two presentations students are expected to participate. Plus points can be awarded for providing quality peer feedback to the presenters.
On the other hand, the project will examine the application of one machine learning model in practice and it should be written as a short paper. The project will have two submissions. The first submission is a midterm report covering 10% of the grade, where the initial results for applying basic benchmark machine learning models are presented. The second submission is the final report accounting for 20% of the portfolio assessment. It should be written as a short paper including a brief literature review, methodology, and results of the final fully tuned model(s) of the selected data sets.
The project can be carried out in a group of 2-3 students and it should include a detailed description of the individual contribution of the participants.
If there is a re-sit examination, the examination form may be changed from written to oral.
Recommended previous knowledge
A good background in linear algebra (matrices and eigenvector), calculus, probability, as well as programming skills in python or MATLAB. Also, taking TPK4186 - Advanced Tools for Performance Engineering or similar courses in the third year would be useful.
Course materials
Textbooks:
- Tom Mitchell: Machine learning, McGraw Hill, 1997
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
Additional course materials (textbook and papers) will be provided in the lectures.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English
Location: Trondheim
- Machine Design and Materials Technology - Materials Production Processes
- Machine Design and Materials Technology - Mechanical Integrity
- Machine Design and Matherials Technology - Products and Machine Design
- Computer and Information Science
- ICT and Mathematics
- Machine Design and Materials Technology
- Computers
- Machine Design and Materials Technology - Mechanical Integrity in Machine Design
- Computer Systems
- Computer Systems
Department with academic responsibility
Department of Mechanical and Industrial Engineering
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 60/100 D PAPIR
-
Room Building Number of candidates - Spring ORD Portfolio 40/100
-
Room Building Number of candidates - Summer UTS School exam 60/100 D PAPIR
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"