2020 Fall, BME Department of Automation and Applied Informatics
Instructors
- Judit Ács
- Ádám Kovács
- Kinga Gémes
Administrative and technical questions should be directed to Judit Acs (acs DOT judit AT aut DOT bme DOT hu)
Official course syllabus and requirements (TAD)
Previous offerings
Schedule
| Week | Lecture | Lab | Deadlines | |||
|---|---|---|---|---|---|---|
| 1 | Sept 9 | Introduction: what is natural language processing, typical applications, history, major areas | Sept 10 | Setting up, git repository, basic exercises, NLP tools | - | |
| 2 | Sept 16 | Built-in types, functions | Sept 17 | Using Jupyter. Writing simple functions. Handling text files. | - | |
| 3 | Sept 23 | Built-in types in details. Advanced string manipulations, regular expressions. | Sept 24 | Typical string manipulation exercises. Writing a simple parser with regular expressions. | - | |
| 4 | Sept 30 | Object-oriented Python, properties, static methods, class methods, magic methods, operator overloading. Iterators, generators. Context managers. | Oct 1 | complex OOP exercises | - | |
| 5 | Oct 7 | Decorators. Functional programming in Python. Writing command line applications in Python. Using Linux command line applications for text processing. IO redirection, pipelines. | Oct 8 | Writing a simple command line application. Using it in the terminal, interacting with built-in Linux commands via pipes. | - | |
| 6 | Oct 14 | Scientific Python. Numpy, scipy. Basic matrix operations. Sparse matrices. | Oct 15 | Matrix manipulation. Working with large sparce matrices. | - | |
| 7 | Oct 21 | NO CLASS | Oct 22 | NO CLASS | - | |
| 8 | Oct 28 | Data science. Handling text data. Basic shell commands. Pandas. | Oct 29 | Handling text data exercises. Data cleaning. Pandas exercisesevaluation loops | - | |
| 9 | Nov 4 | Deep learning for NLP. Feed forward neural networks, recurrent neural networks. LSTM, GRU. | Nov 5 | PyTorch basics. Defining neural networks, training and evaluation loops | ||
| 10 | Nov 11 | NO CLASS | Nov 12 | NO CLASS | - | |
| 11 | Nov 18 | Textual sequence modeling: sequence labeling and classification, sequence-to-sequence models. Attention. | Nov 19 | Sequence modeling in PyTorch. | - | |
| 12 | Nov 25 | Language modeling. Word vectors. Contextualized language models. Transformers. BERT. | Nov 26 | Exploring pretrained models: word2vec, GloVe, fastText, BERT, ELMo | - | |
| 13 | Dec 2 | Dependency parsing. Universal dependencies. | Dec 3 | Working with Universal Dependencies. Multilingual models and problems. | - | |
| 14 | Dec 9 | NLP applications | Dec 10 | Final project presentations | - | |