DTE-2502 Neural Networks - 10 ECTS
The course focus on different approaches into the artificial intelligence domain focusing on neural networks.
The course focus on topics such as application / programming / understanding of concept of a perceptron, the multi-perceptron, feed-forward neural nets, support vector machines (SVM), recurrent neural networks (RNN), convolution neural nets (CNN) and a basic introduction to deep methods. Emphasis will be placed on applications related to image classification and various forms of regressions.
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
|Examination systems:||Date:||Grade scale:|
|Portfolio||Innlevering: 09.12.2022 kl. 15:00||A–E, fail F|
|Coursework requirements – To take an examination, the student must have passed the following coursework requirements:|
|Mandatory exercises||Approved/ Not approved|
Portfolio assessment with the following three components (assessment basis):
- Two programming works (can be submitted in either English or Norwegian)
- One e-test for selected parts of the syllabus
The grade will be based on all three components in the portfolio. The weights of the components will be presented in the introductory presentation.
If more than one component of the portfolio is missing (failed), the candidate will not qualify for a re-sit examination and must take the course again at the next ordinary period.
The re-sit examination consists of completing the portfolio within a new deadline. The component of the portfolio that was missing at the ordinary deadline will be replaced by a new component that must be submitted within the agreed deadline.
- About the course
- Campus: Narvik | Bodø | Annet | Online |
- ECTS: 10
- Course code: DTE-2502
- Responsible unit
- Institutt for datateknologi og beregningsorienterte ingeniørfag
- Tidligere år og semester for dette emnet