Deep learning för industriell bildbehandling
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
- 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.
The course is given in the autumn semester. Application opens mid-March.
Mobyen Uddin Ahmed
021-10 73 69
After submitting your electronic application, the next step is to submit documentation to demonstrate your eligibility for the course you have applied for. In order to document your eligibility, you must provide your high school diploma and university transcript and proof of your English language proficiency.
To meet the entry requirements for this course you need to have previous academic qualifications (university studies). You will find the specific entry requirements above.
No academic qualifications?
If you do not have the formal academic qualifications needed for a specific course, you can apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc. This is also known as a validation of prior learning.
Recognition of prior learning means the mapping out and assessment of an individual's competence and qualifications, regardless how, where or when they were acquired – in the formal education system or in some other way in Sweden or abroad, just recently or a long time ago.
If you think your knowledge and competences will qualify you for this course, you will need to upload th following with your application:
- CV with description of your educational and professional background. Your CV must describe your knowledge and competences in relation to the entry requirements.
- If you refer to work experience, you need to upload an Employers certificate.
If we need more information, we will contact you.
The courses are part of the FutureE project where MDH 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