FYS-3033 Deep learning - 10 ECTS
Knowledge - The student is able to
- describe advanced deep learning techniques
- describe the development of deep learning
- discuss recent developments in the field and develop an understanding for when deep learning might not be the optimal methodology
- discuss advanced deep learning techniques for specialized settings
Skills - The student is able to
- explain the general idea behind deep learning as well as specific algorithms that are being used
- apply the learned material to new applications or problem settings
- use deep learning methodology for research and industrial settings using software libraries such as e.g. Theano or TensorFlow
- carry out an advanced deep learning project after specifications
- make appropriate method and architecture choices for a given application or problem setting
General comtence - The student is able to
- give an interpretation of recent developments and provide an intuition of the open questions in the field
- give an account of the impact of deep learning in modern society and communicate this to non-experts
- implement and apply deep learning methods to applications of her/his choosing
This course is open for inbound exchange students.
Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: INBOUND STUDENT MOBILITY: COURSE COORDINATORS AT THE FACULTIES | UiT
- One mandatory assignment. Access to the examination requires completing/ submitting and passing the assignment.
- A home examinations counting 40%
- An oral examination counting 60 %.
Assessment scale: Letter grades A-F. A is the highest grade and F is failed.
Re-sit examination (section 22): There is no access to a re-sit examination in this course.
For more information, see ichapter 5 in Regulations for examinations at the UiT The arctic university of Norway
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-3033
- Responsible unit
- Institutt for fysikk og teknologi