| Skriv ut |  | Lukk vindu |
Høst 2022
DTE-2502 Neural Networks - 10 stp
The course is administrated by
Type of course
Course contents
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.
Application deadline
Admission requirements
General study qualification with Mathematics R1+R2 and Physics FYS1. Application code: 9391
Recommended prerequisites:
- DTE-2602 Introduction to Machine Learning and Artificial Intelligence
- DTE-2608 Programmering 0
- DTE-2605 Programmering 1
- Experience in Python programming
Objective of the course
On completion of the course, the successful student is expected to have the following:
Knowledge
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.
Skills
The student should be able to:
- Program, adapt and apply neural nets in different application domains.
- Identify and define features in a complex environment.
General Competence
- Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.
Language of instruction
Teaching methods
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.
Date for examination
The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.