Examination arrangement

Course content

- Introduction to deep learning (DL)
- Deep neural networks (DNN)
- Convolutional neural network (CNN)
- Recurrent neural network (RNN)
- Introduction to visual computing
- Still-image and video processing
- Enhancement, filtering and segmentation
- Visual analytics and interactive visualization
- Selected case studies on DL for visual computing

Learning outcome

On successful completion of the module, students will be able to
- Possess advanced knowledge within the area of Deep learning for visual computing. Understand the meaning of concepts such as Multi-layer perceptron, Dropout, Convolutional networks.
- Possess specialized insight and good understanding of the research frontier of Deep learning techniques and algorithms for visual computing applications.

Skills and general competence:
- Be able to use relevant and suitable methods when carrying out further research and development activities in the area of Deep learning for visual computing
- Be able to critically review relevant literature when solving the assigned problem or topic.
- Is able to communicate academic issues, analysis, and conclusions, with specialists in the field, in oral and written forms
- Is experienced in acquiring new knowledge and skills in a self-directed manner
- Develop a course project based on an application scenario and implement several of the algorithms to solve practical problems. The students will also enhance their programming skills in Python and Tensorflow.

Learning methods and activities

Lectures, exercises, self-study, presentation and obligatory course project.
This course will focus on practical implementation of Deep Learning for visual computing.

Further on evaluation

Project report and presentation of the project work

Specific conditions

Recommended previous knowledge

AI or Machine learning (recommended)

Course materials

There is no required textbook and students should be able to learn everything from the lecture notes and course project.