Course - Deep Learning for Visual Computing - IMT4392
IMT4392 - Deep Learning for Visual Computing
About
Examination arrangement
Examination arrangement: Project report and presentation of the project work
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Project report and presentation of the project work | 100/100 |
Course content
Course content(Tentative) :
- Introduction to deep learning (DL)
- Deep neural networks (DNN)
- Convolutional neural network (CNN)
- Recurrent neural network (RNN)
- Transformers, Vision transformers (VIT)
- Generative models,
- Explainable AI
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, and convolutional networks.
- Possess specialized insight and a 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 an 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 Pytorch 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.
Compulsory assignments
- Mid-project presentation
Further on evaluation
The grade is based on the project report and obligatory presentation of the project work.
Specific conditions
Admission to a programme of study is required:
Applied Computer Science (MACS)
Recommended previous knowledge
AI or machine learning (recommended). Familiarity with Python, Pytorch, or Tensorflow, To help students with limited experience in machine learning, we will provide relevant online material (videos, tutorials, and exercises) available at the beginning and set up checkpoint for these basics to ensure that everyone will have the necessary introductory knowledge to work on the course project.
Course materials
There is no required textbook and students should be able to learn everything from the suggested materials and mentoring during the course project.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2024
Language of instruction: English
Location: Gjøvik
- Computer Science
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Project report and presentation of the project work
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Autumn
ORD
Project report and presentation of the project work
100/100
Release
2024-11-25Submission
2024-11-29
09:00
INSPERA
23:59 -
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"