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Neural Networks

Master’s level
7,5 credits (ECTS)


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


A Bachelor's degree in Engineering, Natural or Mathematical sciences of 180 credits including knowledge of Mathematical analysis, Linear algebra and Programming. Applicants must prove their knowledge of English: English 6/English B from Swedish Upper Secondary School or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.