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Vår 2024
FYS-3033 Deep learning - 10 stp
The course is administrated by
Institutt for fysikk og teknologi
Type of course
The course is available as a singular course, also to students enrolled at other universities in Norway, exchange students and Fullbright students. The course will only be taught if there are sufficiently many students. Please contact the student adviser as soon as possible if you are interested in following the course.
Course overlap
FYS-8033 Deep Learning 8 ects
Course contents
Deep Learning, a subfield of machine learning, has in recent years achieved state-of-the-art performance for tasks such as image classification, object detection and natural language processing. This course will study recent deep learning methodology such as e.g. convolutional neural networks, autoencoders and recurrent neural networks, will discuss recent advances in the field, and will provide the students with the required background to implement, train and debug these models. There will be a significant practical component, where students will gain hands-on experience. The course will in addition to deep learning algorithms contain elements of image processing, pattern recognition and statistics.
Admission requirements
Admission requirements are a Bachelor's degree in physics or similar education, including specialization in physics worth the equivalent of not less than 80 ECTS credits. Local admission, application code 9371 - singular course at Master's level.
Objective of the course
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 competence - 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
Language of instruction
The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.
Teaching methods
Lectures: 30 hours
Exercises: 30 hours