Lehrinhalte
[list]
[*]Introduction
[*]Linear methods
[*]Support vector machines
[*]Ensemble methods and boosting
[*]Training and assessment
[*]Unsupervised learning
[*]Neural networks
[*]Convolutional neuronal networks (CNNs)
[*]CNN architectures and applications
[*]Recurrent neural networks (RNNs)
[/list]
Literatur
[list]
[*]T. Hastie et al.: The Elements of Statistical Learning. 2. Aufl., Springer, 2008
[*]I. Goodfellow et al.: Deep Learning. MIT Press, 2016
[*]A. Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. OReilly, 2017
[/list]
Voraussetzungen
Fundamental knowledge in linear algebra and statistics
Preferred: Lecture Fuzzy logic, neural networks and evolutionary algorithms
Online-Angebote
moodle
[list]
[*]Introduction
[*]Linear methods
[*]Support vector machines
[*]Ensemble methods and boosting
[*]Training and assessment
[*]Unsupervised learning
[*]Neural networks
[*]Convolutional neuronal networks (CNNs)
[*]CNN architectures and applications
[*]Recurrent neural networks (RNNs)
[/list]
Literatur
[list]
[*]T. Hastie et al.: The Elements of Statistical Learning. 2. Aufl., Springer, 2008
[*]I. Goodfellow et al.: Deep Learning. MIT Press, 2016
[*]A. Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. OReilly, 2017
[/list]
Voraussetzungen
Fundamental knowledge in linear algebra and statistics
Preferred: Lecture Fuzzy logic, neural networks and evolutionary algorithms
Online-Angebote
moodle
- Lehrende: Jürgen Adamy
- Lehrende: Michael Vogt
Semester: Verão 2019