• This is the 2021 version. For previous year' course materials, go to this branch
• Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
• YSDA homework deadlines will be listed in Anytask (read more).
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
• Installing libraries and troubleshooting: this thread.

• week01 Word Embeddings

  • Lecture: Word embeddings. Distributional semantics. Count-based (pre-neural) methods. Word2Vec: learn vectors. GloVe: count, then learn. Evaluation: intrinsic vs extrinsic. Analysis and Interpretability. Interactive lecture materials and more.
  • Seminar: Playing with word and sentence embeddings
  • Homework: Embedding-based machine translation system

• week02 Text Classification

  • Lecture: Text classification: introduction and datasets. General framework: feature extractor + classifier. Classical approaches: Naive Bayes, MaxEnt (Logistic Regression), SVM. Neural Networks: General View, Convolutional Models, Recurrent Models. Practical Tips: Data Augmentation. Analysis and Interpretability. Interactive lecture materials and more.
  • Seminar: Text classification with convolutional NNs.
  • Homework: Statistical & neural text classification.

• week03 Language Modeling

  • Lecture: Language Modeling: what does it mean? Left-to-right framework. N-gram language models. Neural Language Models: General View, Recurrent Models, Convolutional Models. Evaluation. Practical Tips: Weight Tying. Analysis and Interpretability. Interactive lecture materials and more.
  • Seminar: Build a N-gram language model from scratch
  • Homework: Neural LMs & smoothing in count-based models.

• week04 Seq2seq and Attention

  • Lecture: Seq2seq Basics: Encoder-Decoder framework, Training, Simple Models, Inference (e.g., beam search). Attention: general, score functions, models. Transformer: self-attention, masked self-attention, multi-head attention; model architecture. Subword Segmentation (BPE). Analysis and Interpretability: functions of attention heads; probing for linguistic structure. Interactive lecture materials and more.
  • Seminar: Basic sequence to sequence model
  • Homework: Machine translation with attention

• week05 Transfer Learning

  • Lecture: What is Transfer Learning? Great idea 1: From Words to Words-in-Context (CoVe, ELMo). Great idea 2: From Replacing Embeddings to Replacing Models (GPT, BERT). (A Bit of) Adaptors. Analysis and Interpretability. Interactive lecture materials and more.

• week06 Domain Adaptation

  • Lecture: General theory. Instance weighting. Proxy-labels methods. Feature matching methods. Distillation-like methods.
  • Seminar+Homework: BERT-based NER domain adaptation

• week07 Model compression and acceleration

• week08 Probabilistic inference, generative models and hidden variables

• week09 Machine translation

• week10 Relation extraction

• week11 Summarization

• week12 Style Transfer

• week13 Dialogue systems

• week14 AI & ML generated art

Course materials and teaching performed by

• Elena Voita - course admin, lectures, seminars, homeworks
• Boris Kovarsky - lectures, seminars, homeworks
• David Talbot - lectures, seminars, homeworks
• Just Heuristic - lectures, seminars, homeworks
• Alexey Tikhonov @altsoph
• Michael Sejr Schlichtkrull
• Arthur Bražinskas
• Ivan Yamshchikov
• Nikolay Zinov

• Sergey Gubanov
• Vyacheslav Alipov
• Vladimir Kirichenko
• Andrey Zhigunov
• Pavel Bogomolov