DTE-2502 Neural Networks - 10 ECTS
On completion of the course, the successful student is expected to have the following:
The student will have:
- An overview of history and numerous approaches within machine learning neural nets.
- Understanding of "The curse of dimensionality" in AI.
- Basic understanding of back-propagation and complexity.
The student should be able to:
- Program, adapt and apply neural nets in different application domains.
- Identify and define features in a complex environment.
- Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.
The subject uses so-called "Flipped classroom", i.e., lectures are posted online continuously during the semester in the form of short instructional videos and demonstrations of 10-20 minutes. In addition, exercises and control questions related to each video are used.
The subject teaches in the autumn semester with teacher-led and assistant-led learning and / or exercises. Online students will have access to a teaching assistant for afternoon / evening support.
This course is available for inbound exchange students.
There are no academic prerequisites to add this module in your Learning Agreement.
DTE-2602 Introduction to Machine Learning and Artificial Intelligence
DTE-2608 Programmering 0
DTE-2605 Programmering 1
Experience in Python programming
Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412.
Deadline: 15th April
Course work requirement (work requirements)
- Mandatory exercises: 4 of 6 approved exercises (Pass / Fail)
Examination and assessment:
Folder assessment with the following content (assessment basis):
- Mandatory work: 2 programming works (Grade: A-F)
- Multiple choice test for parts of the syllabus (Grade: A-F)
Work and multiple-choice tests can be weighted differently in their contribution to the final grade in the course.
If the work requirements are not met (delivered and passed at least 4 of 6 submissions) the candidate will not qualify to get a grade in the course.
If more than 30% of the portfolio (assessment basis) is missing (i.e., has passed less than 70% of the portfolio), the candidate will not qualify for continuation and must re-take the course at the next ordinary period.
The portfolio (exercises and tests) can be delivered in Norwegian or English.
Continuation consists of completing the folder within a deadline. The parts of the work requirements that were missing during ordinary deadline will be replaced by new corresponding work requirements that must be made within the agreed deadline.
- About the course
- Campus: Narvik | Bodø | Annet | Digitalt |
- ECTS: 10
- Course code: DTE-2502
- Responsible unit
- Institutt for datateknologi og beregningsorienterte ingeniørfag