A course in reinforcement learning in the wild

YSDA Natural Language Processing course

• This is the 2019 version. For previous year' course materials, go to this branch
• Lecture and seminar materials for each week are in ./week* folders
• 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.

Syllabus

• 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. Analysis of attention heads in Transformer.
  • Seminar: Machine translation of hotel and hostel descriptions

• week05 Expectation-Maximization

  • Lecture: Expectation-Maximization and Hidden Markov Models
  • Seminar: Implementing expectation maximization

• week06 Machine Translation

  • Lecture: Word Alignment Models, Noisy Channel, Machine Translation.
  • Seminar: Introduction to word alignment assignment.

Contributors & course staff

Course materials and teaching performed by

• Elena Voita - course admin, lectures, seminars, homeworks
• Boris Kovarsky - lectures, seminars, homeworks
• David Talbot - lectures, seminars, homeworks
• Sergey Gubanov - lectures, seminars, homeworks
• Just Heuristic - lectures, seminars, homeworks