Natural Language Processing
The course covers the main tasks and mathematical methods of natural language processing.
- Preprocessing, feature extraction and classification.
- Vector representations of words.
- The task of marking sequences (tagging). The Linear-CRF model.
- Recurrent neural network models: GRNN, LSTM.
- Machine translation. The Sequence-to-sequence approach. The mechanism of attention.
- Transformer architecture.
- The task of language modeling.
- Statistical and neural network language models.
- The task of generating a natural language.
- Contextual vector representations of words.
- Transfer learning to NLP.
- The BERT model and its modifications.
- Text classification.
- Topic modeling and its applications.
The course includes four practical tasks and an exam. The deadline for completing each task is 2 weeks. You can get up to 10 points for each task. For each day of delay, a penalty of 1 point is assigned.
0.7 * hw points / 5 + 0.3 * exam points.
Machine learning, deep learning, Python.