Level: Undergraduate · Duration: 13 weeks, one weekly session · Approach: code-first, project-based
Lecturer: Dr. Alexander (Sasha) Apartsin · Course text: llmbook.icsgen-ai.org (open access) · ebook at Amazon
Modern Natural Language Processing (NLP) is a field within artificial intelligence that focuses on enabling machines to understand, process, and generate human language in both written and spoken forms. It covers a broad range of tasks, including text classification, text summarization, text translation, question answering, and dialogue systems. With the rise of multimodal AI, which integrates language with other data types such as images, audio, and video, language has become a crucial component in tasks like text-to-image generation and image captioning.
While all necessary background material, including machine and deep learning fundamentals and the PyTorch library, will be introduced during the course, students are expected to have a solid understanding of foundational machine learning concepts and proficiency in Python. Prior experience with deep learning is recommended and will be beneficial.
Graduates of the course will acquire a broad and in-depth understanding of techniques for constructing, training, and applying large language models. Specifically, students will be able to:
The course combines lectures with student presentations. Ten sessions are lectures on the week's theme, introducing concepts and applications through practical, code-first examples using modern libraries and tools. The remaining three sessions (Weeks 5, 9, and 13) are dedicated to student presentations of the course project: proposal, interim report, and finals. In all three, teams present and receive in-class feedback from the lecturer and peers.
During the course, students will propose, implement, and present an innovative project that builds on the concepts covered in class. The project will focus on a task of the student's choice, involving the generation of synthetic training data and the comparison of multiple LLMs, including both off-the-shelf and fine-tuned models. Each project team will give three in-class presentations: a project proposal, an interim progress report, and a final presentation. By the end of the course, teams will submit a GitHub repository containing all project materials, including presentation slides, source code, datasets, and a detailed README file. For inspiration, a sample of past student projects supervised by the instructor is available at apartsin.com/teaching/student-projects.
Below is an approximate weekly schedule outlining the subjects that will be covered. The actual order or content may vary depending on the class background and recent advancements in the field. Chapter numbers refer to the course text [1]; each entry links to the corresponding chapter. Presentation weeks are highlighted.
| Week | Theme and readings |
|---|---|
| 1 | Course Introduction Course and project requirements; project examples; basic AI concepts refresher. |
| 2 | Natural Language Processing Typical NLP tasks; cyber, healthcare, and software engineering use cases; tokenization, term and topic vectors, topic modelling; word embeddings. Readings [1]: Ch. 1, Foundations of NLP & Text Representation |
| 3 | Introduction to Foundation LLMs LLM libraries and APIs; text representation and generation; text decoding, prompt engineering, and model fine-tuning. |
| 4 | Synthetic Data Generation for Model Training and Evaluation Frameworks for synthetic data generation: DataDreamer, Curator; attributed and bootstrapped data generation; weak supervision; data anonymization; model evaluation, text generation metrics, and LLM judges. |
| 5 | Student Presentations I: Project Proposal Each team presents its project proposal and receives in-class feedback from the lecturer and peers. |
| 6 | Building and Training Transformer-Based Models Attention, cross-attention, multi-headed attention; transformer blocks, encoder-decoder transformers; transformer pretraining; sentence embedding; instruction following; fine-tuning for human preferences; Seq2Seq models; mixture of experts; Music Transformer; explaining transformers. |
| 7 | LLM Fine-Tuning and Transfer Learning PEFT: adapters, quantization, QLoRA; soft prompts and p-tuning; representation fine-tuning: denoising autoencoder, SimCSE; sequence and token classification fine-tuning: NER, SetFit; long texts and Longformer; model distillation and merging. |
| 8 | LLMs for Information Retrieval and Extraction Vector stores: FAISS, product quantization, chunking; RAG: DPR, chunking, HyPE, HyDE, MMR, RAG evaluation; topic modelling and recommendation systems; knowledge graphs: information extraction and KG-assisted generation. |
| 9 | Student Presentations II: Interim Report Each team presents its interim progress and receives in-class feedback from the lecturer and peers. |
| 10 | Agentic AI and LLM-Based Multi-Agent Systems Tools: function calls, MCP, Toolformer; memory: MemGPT and conversational buffers; agent flows and planning: ReAct, Reflection, Agentic RAG, BabyAGI; multi-agent systems: AutoGen, CrewAI. |
| 11 | Visual Language Models Vision Transformer; multimodal models: CLIP, BLIP-2; document models: LayoutLM; Segment Anything. |
| 12 | Advanced Topics LLM safety: jailbreaking and watermarking; LLMs for tabular data: Text2SQL; LLMs for software engineering: requirements engineering, QA, design. |
| 13 | Student Presentations III: Project Finals Conference-style final talks with in-class feedback. Project repositories (slides, source code, datasets, and README) are submitted at the end of the course. |
| Delivery | Grade |
|---|---|
| In-class project proposal presentation | No grade, for feedback and approval only |
| In-class project interim presentation | 20% |
| In-class final project presentation | 40% |
| Final project submission (GitHub repository) | 40% |
The use of AI tools for generating project code and presentation slides is highly encouraged, provided that two essential requirements are met: the project must demonstrate novelty by addressing a new and valuable task, and the team must maintain full ownership and responsibility for all submitted code and presentation materials.
[1] A. Apartsin and Y. Aperstein, Building Language AI: From Tokens to Agents, 16th ed., 2026. Open access online at https://llmbook.icsgen-ai.org; also available as an ebook at Amazon: https://www.amazon.com/dp/B0H1MQH23D.
[2] J. Alammar and M. Grootendorst, Hands-On Large Language Models, 2024.
[3] S. Raschka, Build a Large Language Model (From Scratch), 2024.
[4] L. Tunstall, L. von Werra, and T. Wolf, Natural Language Processing with Transformers, 2022.
[5] M. Lanham, AI Agents in Action, 2025.
[6] U. Kamath, K. Keenan, G. Somers, and S. Sorenson, Large Language Models: A Deep Dive — Bridging Theory and Practice, 2024.