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
Portfolio assessment of project assignments counting about 40 % and a final oral examination counting about 60 %. All modules in the portfolio are assessed as a whole and one combined grade is given.
Assessment scale: Letter grades A-F. F - fail.
Re-sit examination (section 22): There is no access to a re-sit examination in this course.
Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Both postponed laboratory exercises and postponed oral examination are arranged during the semester if possible, otherwise early in the following semester. See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-3033
- Responsible unit
- Institutt for fysikk og teknologi