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Morten Hjorth-Jensen
2021-07-11 22:06:16 +02:00
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@@ -24,7 +24,7 @@ This course aims at giving you insights and knowledge about many of the central
- Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
- Learn about about decision trees, random forests, bagging and boosting methods;
- Learn about support vector machines and kernel transformations;
- Reduction of data sets, from PCA to clustering;
- Reduction of data sets and unsupervised learning, from PCA to clustering;
- Autoencoders and Reinforcement Learning;
- Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).