 |
| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2021W |
| Objectives |
In this class, students will learn about Recurrent neural networks (RNNs) and Long Short Term Memory (LSTM), types of neural networks that are very commonly used for predicting sequential data, such as time-series, text or DNA. We will cover both theoretical fundamentals and advanced applications. It is expected that students visiting this class already have a solid understanding of deep learning.
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| Subject |
- Basics of sequence prediction
- Recurrent neural networks
- Vanishing Gradients and LSTMs
- Attention Models
- Memory Models (e.g. Neural Turing Machine)
- Autoregressive Sequence Models and Transformers (PixelRNN, ByteNet, Transformers)
- Applications in NLP (e.g. next character prediction, language modelling, sentiment analysis)
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| Criteria for evaluation |
Exam
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| Language |
English |
| Study material |
Lecture notes and slides
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| Changing subject? |
No |
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