Kursinformation

Lehrinhalte
[list]
[*]Concepts of machine learning
[*]Linear methods
[*]Support vector machines
[*]Trees and ensembles
[*]Training and assessment
[*]Unsupervised learning
[*]Neural networks and deep learning
[*]Convolutional neuronal networks (CNNs)
[*]CNN applications
[*]Recurrent neural networks (RNNs)
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Literature
[list]
[*]T. Hastie et al.: The Elements of Statistical Learning. 2nd ed., Springer, 2008
[*]I. Goodfellow et al.: Deep Learning. MIT Press, 2016
[*]A. Géron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 2nd ed., O’Reilly, 2019
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Voraussetzungen
Fundamental knowledge in linear algebra and statistics
Preferred: Lecture “Fuzzy logic, neural networks and evolutionary algorithms”

Online-Angebote
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