Content

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

Schedule

• 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.

Literature

• 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