Course - Deep learning for visual computing - IMT4392
IMT4392 - Deep learning for visual computing
About
New from the academic year 2019/2020
Examination arrangement
Examination arrangement: Report
Grade: Letters
| Evaluation form | Weighting | Duration | Examination aids | Grade deviation |
|---|---|---|---|---|
| Approved report | 100/100 |
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
Admission to a programme of study is required:
Applied Computer Science (MACS)
Applied Computer Science (MACS-D)
Colour in Science and Industry (COSI) (MACS-COSI)
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.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2019
Language of instruction: English
Location: Gjøvik
-
Examination
Examination arrangement: Report
- Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
-
Autumn
ORD
Approved report
100/100
Submission 2019-12-02
Submission 08:00
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"