YSDA Natural Language Processing course

• This is the 2025 iteration of the course, materials are added as we prepare them.
• Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
• Installing libraries and troubleshooting: this thread.

Syllabus

• 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 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.

• week03 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

• week04 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.
  • Homework: fine-tuning a pre-trained BERT model

• week05 Large Language Models

  • Lecture: Scaling laws. Emergent abilities. Open-source LLMs.
  • Practice: hands-on with open-source LLMs

• week06 Prompting & In-Context Learning

  • Lecture: Prompting techniques. Chain-of-Thought reasoning. In-context learning: how and why it works. Analysis and Interpretability.
  • Homework: manual prompt engineering and chain-of-thought reasoning

• week07 Fine-tuning (PEFT & RLHF)

  • Lecture: Parameter-efficient fine-tuning (LoRA, adapters). Reinforcement Learning from Human Feedback (RLHF).
  • Seminar + Homework

• week08 Efficiency

  • Lecture: Quantization. Distillation. Pruning. Speculative decoding.
  • Homework

• week09 Retrieval-Augmented Generation (RAG)

  • Lecture: Dense retrieval. RAG architectures.
  • Practice

• week10 AI Agents

  • Lecture: Agent architectures. Tool use. Memory.
  • Seminar + Homework

• week11 Interpretability

  • Lecture: Probing. Mechanistic interpretability.
  • Seminar + Homework

• week12 Multimodal LLMs

• week13 Building LLM Systems

• week14 AI Agents in Production

Contributors & course staff

Course materials and teaching performed by

• Elena Voita - original course author
• Michael Diskin - responsible for the 2025 edition; lecturer
• Just Heuristic - most of the seminars and some lectures
• Ignat Romanov, George Yakushev, Andrei Panferov - lectures
• Natasha Badanina - course admin for on-campus students
• Boris Kovarsky, David Talbot, Sergey Gubanov, Ruslan Svirschevski - help build course materials and/or held some classes
• 30+ volunteers who contributed and refined the notebooks and course materials. Without their help, the course would not be what it is today
• A mighty host of TAs who stoically grade hundreds of homework submissions from on-campus students each year