course-details-portlet

IMT4392

Deep Learning for Visual Computing

Choose study year
Credits 7.5
Level Second degree level
Course start Autumn 2024
Duration 1 semester
Language of instruction English
Location Gjøvik
Examination arrangement Project report and presentation of the project work

About

About the course

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)
Miscellaneous Courses - Faculty of Information Technology and Electrical Engineering (EMNE/IE)

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.

Subject areas

  • Computer Science

Contact information

Course coordinator

Department with academic responsibility

Department of Computer Science