Artificial neural networks
Course
FYM135
Master’s level
7.5 credits (ECTS)
Offered by the
Department of Physics
at the
Faculty of Science and Technology
About
The course gives an overview and a fundamental theoretical understanding of the most important neural net algorithms. These include models of associative memory algorithms for learning from examples (e.g., perceptron learning, back-propagation, temporal difference learning), and models for self-organization. Through comparison with methods from statistics and computer science students can develop an understanding of when neural networks are useful in application problems.
Prerequisites and selection
Entry requirements
Bachelor degree, inclusing at least 30 credits maths, plus programming.
Selection
Selection is based upon the number of credits from previous university studies, maximum 165 credits.
