Throughout the course we will use the Python Programming Language for in-class exercises and homeworks, augmented with a range of special packages and libraries, such as the Natural Language ToolKit (NLTK) for text processing and PyTorch for Neural Networks. Students' projects may be scaled to their level of programming expertise.

Basic coding skills are essential for success in this course, but no special preparation in Natural Language Processing or Machine Learning is required. Students without any prior programming experience are encouraged to take COGS 2500Q ("Coding for Cognitive Science") prior to this course.

Course Objectives: By the end of this course, students will have achieved the following:

• an understanding of computational linguistics and natural-language processing: goals, typical tasks, and common challenges
• the ability to perform basic coding tasks (e.g., obtain and manipulate data, implement algorithms, use Python modules and libraries)
• a basic understanding of logical, mathematical, and statistical concepts that are commonly applied in language processing, and the ability to apply them
• the ability to formulate research questions and hypotheses, test them and communicate the results.

• Homeworks must be submitted before the beginning of class on the due date. Students are allowed (in fact, encouraged) to collaborate on homeworks, but each student must submit their own answers and state with whom they collaborated.
• The final project will be an extended programming exercise that expands on one of the topics covered in class. For instance, we'll be covering grammars and syntactic parsers, so students could consider writing a grammar and parser for a language other than English (your choice), implementing a parsing algorithm that we didn't cover in class, adding functionality or a learning module, and more. Students are encouraged to work in small teams (2-3 people) on this. Towards the end of the semester, teams will give a brief (5-10min) presentation. For final evaluation, teams must submit their code, the materials used in the presentation (e.g., slides, handouts), and a brief (1-3 pages) writeup of the project. More details will be discussed in class.
• Participation is an important part of the evaluation. This includes requirements that can only be met in class (such as discussions and exercises). Therefore attendance is crucial. Students who must miss classes due to health problems or other unavoidable reaons must give me advance notice. Temporary accommodations (such as following lectures online) are possible in certain cases, but must be arranged in advance.

• Raschka, S. 2024. Build a Large Language Model (From Scratch).
  A hands-on guide to implementation of GPT-2 style language model, covering all the crucial ingredients. Includes a good introduction to PyTorch, a popular Python library used to build and train Neural Networks.
• Jurafsky, D. and J.H. Martin. 2009. Speech and Language Processing. 3rd edition. Prentice Hall
  An introduction to most of the theoretical background necessary to understand how Large Language Models are built, trained, and applied.
  This new and thoroughly revised edition of a popular textbook. It has been in the works for many years and seems to be nearing completion, but it is not yet available for purchase. The most recent online version is from January 6, 2026. We will be using chapters from this book, as well as some parts of the earlier edition and other readings. Those will be available online and/or on HuskyCT.