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Current issues, methods and datasets of unsupervised learning in image and text processing, including LSTMs, transformers, generative models.


  • Start: Tuesday, 12.10.2021, 2-4pm
  • Lecture: Tuesday,  from 12.10.2021, 2-4pm
  • Exercise: Monday, from 18.10.2021, 12am-2pm


Qualification Goals

  • Deep understanding of current methods of unsupervised learning of image and text representations, self-supervised learning, representation learning, generative models.
  • Understand, apply and evaluate current approaches.
  • Understanding the technical underpinnings of unsupervised learning methods.
  • Evaluate and discuss new learning problems and unsupervised and self-supervised methods.


  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, An MIT press book, 2016.
  • Attention and Augmented Recurrent Neural Networks, Chris Olah and Shan Carter. Distill, 2016
  • Generating Sequence with Recurrent Neural Networks, A. Graves, ArXiV