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Jan. 23, 2019
Sep. 8, 2018

YSDA Natural Language Processing course

  • Lecture and seminar materials for each week are in ./week* folders
  • Create cloud jupyter session from this repo - Binder
  • YSDA homework deadlines are listed in Anytask course page.
  • Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
  • Installing libraries and troubleshooting: this thread.


  • week01 Embeddings

    • Lecture: Word embeddings. Distributional semantics, LSA, Word2Vec, GloVe. Why and when we need them.
    • Seminar: Playing with word and sentence embeddings.
  • week02 Text classification

    • Lecture: Text classification. Classical approaches for text representation: BOW, TF-IDF. Neural approaches: embeddings, convolutions, RNNs
    • Seminar: Salary prediction with convolutional neural networks; explaining network predictions.
  • week03 Language Models

    • Lecture: Language models: N-gram and neural approaches; visualizing trained models
    • Seminar: Generating ArXiv papers with language models
  • week04 Seq2seq/Attention

    • Lecture: Seq2seq: encoder-decoder framework. Attention: Bahdanau model. Self-attention, Transformer. Pointer networks. Attention for analysis.
    • Seminar: Machine translation of hotel and hostel descriptions
  • week05 Structured Learning

    • Lecture: Structured Learning: structured perceptron, structured prediction, dynamic oracles, RL basics.
    • Seminar: POS tagging
  • week06 Expectation-Maximization

    • Lecture: Expectation-Maximization and Word Alignment Models
    • Seminar: Implementing expectation maximizaiton
  • week07 Machine translation

    • Lecture: Machine Translation: a review of the key ideas from PBMT, the application specific ideas that have developed in NMT over the past 3 years and some of the open problems in this area.
    • Seminar: presentations by students
  • week08 Transfer learning and Multi-task learning

    • Lecture: What and why does a network learn: "model" is never just "model"! Transfer learning in NLP. Multi-task learning in NLP. How to understand, what kind of information the model representations contain.
    • Seminar: Improving named entity recognition by learning jointly with other tasks
  • week09 Domain Adaptation

    • Lecture: General theory. Instance weighting. Proxy-labels methods. Feature matching methods. Distillation-like methods.
    • Seminar: Adapting general machine translation model to a specific domain.
  • week10 Dialogue Systems

    • Lecture: Task-oriented vs general conversation systems. Overview of a framework for task-oriented systems. General conversation: retrieval and generative approaches. Generative models for general conversation. Retrieval-based models for general conversation.
    • Seminar: Simple retrieval-based question answering
  • week11 Adversarial learning & Latent Variables for NLP

    • Lecture: generative models recap, generative adversarial networks, variational autoencoders and why should you care about them.
    • Seminar: semi-supervised dictionary learning with adversarial networks
  • week12-13 - TBA

Contributors & course staff

Course materials and teaching performed by