COE4212804  - Introduction to Natural Language Processing - Spring 2026

Prerequisite Courses

Data Structures

Required Text(s)

 

Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin (html).

Recomended Text(s)

- Jacob Eisenstein, Natural Language Processing (2018) 
- Yoav Goldberg, Neural Network Methods for Natural Language Processing (2017)
- James Pustejovsky and Amber Stubbs, Natural Language Annotation for Machine Learning (2012)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (https://github.com/ageron/handson-ml3)
- Deep Learning for Coders with fastai and PyTorch (https://github.com/fastai/fastbook)
- HuggingFace NLP Course (html)
- Practical Natural Language Processing (2020) (https://github.com/practical-nlp)
- Natural Language Processing with Transformers, Revised Edition (2022) (https://github.com/nlp-with-transformers )
- Natural Language Processing with PyTorch (2019) (https://github.com/delip/PyTorchNLPBook)

Meeting Times:

- Thusday 14:30 - 17:30,  Location: C-210

Labs:

- Lab : No labs

Tentative Grading:

Evaluation Tool (*)	Weight in %
Assignments, Presentations and Projects	30
In-term Exams - 1 Midterm	30
Final	40

(*) After each type of assesment, some of the students may be called for an oral examination. The student's performance in the oral exam will affect the student's grades. If a student does not come for an oral exam or follow the specified exam rules, (s)he will get automatically score 0 (zero) points for that part of the assesment.

Tentative  Course Outline:

WEEK	TOPIC(S)
1	Introduction
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3
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15