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FMG3100 Basic Applications of Neural Networks in Manufacturing 1.5 credits

The course will focus on the application of neural networks in manufacturing.

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Headings with content from the Course syllabus FMG3100 (Spring 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Engineering is experiencing a drastic change due to the ubiquitous presence of computers and internet. This fact has triggered the designation of this phenomenon as the fourth industrial revolution, and the application of the associated term Industry 4.0. One of the important components envisioned in the Industry 4.0 is artificial intelligence. It is a field within computer science that has achieved a stepwise progress in the last half a century. The last of these steps is associated with Deep learning. It is a field within machine learning that relies on artificial neural networks with multiple layers. The course aims at giving the basic understanding of an artificial neural network operation and enabling students to use the more complex neural networks for their research. The activities are tailored for PhD students in the domain of manufacturing.

Intended learning outcomes

After successful completion of this course, the students will be able to:

  • Based on a case study proposed by the course leader, program a very basic neural network from scratch in Python
  • Contextualize and use more complex neural networks with the help of Neurolab and Keras libraries
  • Discuss and describe basic application of machine vision

Course disposition

The course is composed of four meetings (each one will last approximately two hours). Each meeting will be of a lecture/tutorial type. The self-work in the range of about 32 hours will be devoted to:

  • study of relevant literature
  • programming
  • project report (about possible application of neural network within your PhD topic)

Literature and preparations

Specific prerequisites

PhD student

Recommended prerequisites

Basic knowledge of programming in any computer language, but Python is the preferred choice. The students should have some familiarity with the basic concepts of multivariate analysis and linear algebra (mainly matrix manipulation).


No information inserted


The course is based on hand-outs from the course leader that will be provided along the course

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • PRO1 - Project, 1.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The assessment of students’ work will be based mainly on the project report. If deemed appropriate students will be encouraged to publish a journal paper (with the help of the supervisors).

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Profile picture Antonio Maffei

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FMG3100

Offered by

ITM/Production Engineering

Main field of study

No information inserted

Education cycle

Third cycle

Add-on studies

No information inserted

Postgraduate course

Postgraduate courses at ITM/Production Engineering