course: Artificial Neural Networks
- teaching methods:
- lecture with tutorials
- overhead transparencies, computer based presentation, black board and chalk
- responsible person:
- Prof. Dr. Sen Cheng
- Prof. Dr. Sen Cheng (Neuroinformatik)
- offered in:
- winter term
dates in winter term
- start: Monday the 26.10.2020
- lecture: siehe "Sonstiges"
Die Angaben zu den Prüfungsmodalitäten (im WiSe 2020/2021 | SoSe 2021) erfolgen vorbehaltlich der aktuellen Situation. Notwendige Änderungen aufgrund universitärer Vorgaben werden zeitnah bekanntgegeben.
Termin wird vom Dozenten bekannt gegeben
|Form of exam:||written|
|Registration for exam:||FlexNow|
Artificial neural networks (ANN) were inspired by the architecture and function of the brain. Nevertheless, their greatest strength is not that they are good models of the brain, but rather that they are powerful function approximators. Since the 1980's many types of ANN have been developed and tricks for training ANNs on data proliferated. Recent advances in computing hardware and the availability of large datasets has made it possible to train ANNs such that they perform better than humans, e.g., on image recognition. In this class, students will, firstly, gain a theoretical understanding of the principles underlying the methods applied to neural networks and, secondly, learn practical skills in implementing neural networks and applying them for data analysis.
- optimization problems
- logistic regression
- biological neural networks
- model selection
- universal approximation theore
- deep neural networks
- recurrent neural networks
- Hopfield network
- Bolzmann machine
- linear algebra
- Géron, Aurélien "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems", None, None
- Aggarwal, Charu C. "Neural Networks and Deep Learning", Springer Verlag, None https://link.springer.com/download/epub/10.1007%2F978-3-319-94463-0.epub
Please register for the tutorials in advance!
Link to the Moodle course: https://moodle.ruhr-uni-bochum.de/m/enrol/index.php?id=22627