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Deep Learning for Industrial Imaging

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

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 this course

Lesson 1 - Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques.
Lesson 2 - Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture.
Lesson 3 - Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

What 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

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 this course

Lesson 1 - Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques.

Lesson 2 - Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture.

Lesson 3 - Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

What 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.

Language

English

Occasions for this course

Autumn semester 2021

  • Autumn semester 2021

    Scope

    2.5 credits

    Time

    2021-10-11 - 2021-11-28 (part time 25%)

    Education ordinance

    Second cycle

    Course type

    Independent course

    Application code

    MDH-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

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