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 have 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.
Topics: optimization problems, regression, logistic regression, biological neural networks, model selection, universal approximation theorem, perceptron, MLP, backpropagation, deep neural networks, recurrent neural networks, LSTM, Hopfield network, Bolzmann machine
Software: python, numpy, scipy, matplotlib, scikit-learn, tensorflow
There will be a written examination at the end of the course.
- Course type
- 6 CP
- Winter Term 2020/2021
moodle course available
every week on Monday from 16:00 to 18:00.
First appointment is on 26.10.2020
Last appointment is on 08.02.2021
every week on Friday from 10:00 to 12:00.
First appointment is on 06.11.2020
Last appointment is on 12.02.2021
every week on Tuesday from 12:00 to 14:00.
First appointment is on 27.10.2020
Last appointment is on 09.02.2021
every week on Wednesday from 10:00 to 12:00.
First appointment is on 28.10.2020
Last appointment is on 10.02.2021
Calculus, linear algebra, statistics, programming.