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Deep Learning for Industrial Imaging

  • Credits 2.5  credits
  • Education level Second cycle
  • Study location Distance with no obligatory meetings
  • Course code DVA476
  • Main area Computer Science

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.

 

Requirements

Below you find the entry requirements for the course. If you do not fulfill the requirements, you can get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etcetera. Read more in Application information below.

Occasions for this course

Autumn semester 2024

  • Autumn semester 2024

    Scope

    2.5 credits

    Time

    2024-10-14 - 2024-12-01 (part time 25%)

    Education level

    Second cycle

    Course type

    Freestanding course

    Application code

    MDU-24540

    Language

    English

    Study location

    Independent of location

    Teaching form

    Distance learning
    Number of mandatory occasions including examination: 0
    Number of other physical occasions: 0

    Course syllabus & literature

    See course plan and literature list (DVA476)

    Specific requirements

    90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

    Selection

    University credits

Questions about the course?

If you have any questions about the course, please contact the Course Coordinator.

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 can validate work experience to determine whether you have the qualifications 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, 45.5 kB, opens in new window.

Download a template for Employers certificate Word, 38 kB, opens in new window.

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