Deep Learning for Industrial Imaging

  • Credits 2.5 credits
  • Study location Independent of location
  • $stringTranslations.StartDate 2022-10-10 - 2022-11-27 (part time 25%)
  • $stringTranslations.StartDate 2022-10-10 - 2022-11-27 (part time 25%)
  • Education level Second cycle
  • Course code DVA476
  • Main area Computer Science
  • Application code: MDU-24540

This course will teach you how to build convolutional neural networks. You will learn to design intelligent systems using deep learning for classification, annotation, and object recognition.

About the course

The course includes three modules:

Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques

Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture.

Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.


You will learn:

  • Understand the fundamental theory of image processing.
  • Able to describe the fundamental needs, challenges and limitations of Big data with industrial imaging.
  • Able to describe and understand the basic principles of convolution neural network.
  • Demonstrate the ability to use tools for deep learning in industrial imaging

Entry requirements

90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming. In addition English course A/English course 6 is required.

You can also apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc.




Course syllabus

You can read in detail about the course, it's content and literature and so on in the course syllabus

See course syllabus

Apply to the course

Deep learning for industrial imaging

Go to application

Application information

You’ll find the entry requirements in the course description. After submitting your application, the next step is to submit documentation to demonstrate your eligibility for the course. Most academic credentials from Sweden are retrieved automatically. Wait a few days after submitting your application - if you still can’t see your academic credentials om My pages, please upload them.

If you have studied in another country, you must provide transcripts of your academic studies and of your English proficiency. Exactly what you need to submit and how, depends on several factors. You can read more on universityadmissions.se or antagning.se.

If the course requires work experience, you need to provide an employer’s certificate. You can download a template for employer’s certificate below.

No academic qualifications?

Many courses requires that you have previous academies studies, but we validate work experience to assess if you have the knowledge that is equal to the eligibility requirements for the course.

If you don’t have the formal qualifications required, please send in a certificate of employment (current or previous) and a CV/Description of competence that describes your educational and professional background. Please include a short description of your work experience, not only the work title.

Use the CV/ Description of competence template below and fill in the information requested.

You can also use our template for Employers certificate if you like.

Download a template for CV/Description of competence Word, 28 kB, opens in new window.

Download a template for Employers certificate Word, 19.5 kB, opens in new window.

If you have any questions regarding eligibility or application please send an e-mail to lifelonglearning@mdu.se


The courses are part of the FutureE project where MDU offers online courses in the areas of AI, Environmental and Energy Engineering, Software and Computer Systems Engineering.

For companies that want to collaborate on competence development