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General Information

This course replaces Machine Learning (573050).

Suggested prerequisites: Mathematics I to IV, basic knowledge in Python.

Exam: written examination (90 minutes), 5 ECTS.

Contact: michael dot teichmann at informatik dot tu-chemnitz dot de.

Language: English. The exam can be done in German.

Registration and materials: https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/21637267460


The course will introduce a variety of methods using neural networks to learn to solve useful problems. The first part of the course covers the deep learning area, starting from an introduction to machine learning and old-school neural networks up to current research trends. The second part will introduce other forms of neural network structures (attractor networks, reservoir computing, unsupervised Hebbian learning and spiking networks) which may be used to build complex cognitive architectures.

The different algorithms presented during the lectures will be studied in more details during the exercises, through implementations in Python (tensorflow, keras).

The plan of the course is:

  1. Linear algorithms (regression, classification)
  2. Neural Networks (MLP, regularization)
  3. Deep Learning (CNN, Autoencoder, GAN)
  4. Recurrent neural networks (LSTM, attention, Transformer)


  • Deep Learning, Ian Goodfellow, Yoshua Bengio & Aaron Courville, MIT press.


  • I do not have Neurocomputing in my study program, can I take it anyway?
    If you have Machine Learning (573050) in your program, you can take Neurocomputing instead.