Course Homepage

Course Description

Natural language processing is ubiquitous in modern intelligent technologies, serving as a foundation for language translators, virtual assistants, search engines, and many more. In this course, we cover the foundations of modern methods for natural language processing, such as word embeddings, recurrent neural networks, transformers, and pretraining, and how they can be applied to important tasks in the field, such as machine translation and text classification. We also cover issues with these state-of-the-art approaches (such as robustness, interpretability, sensitivity), identify their failure modes in different NLP applications, and discuss analysis and mitigation techniques for these issues.

Class

Platform	Where & when
Lectures	Wednesdays: 11:15-13:00 [STCC - Cloud C] & Thursdays: 13:15-14:00 [CE16]
Exercises Session	Thursdays: 14:15-16:00 [CE11]
Project Assistance (not every week)	Wednesdays: 13:15-14:00 [STCC - Cloud C]
QA Forum & Annoucements	Ed Forum [link]
Grades	Moodle [link]
Lecture Recordings	Mediaspace [link]

All lectures will be given in person and live streamed on Zoom. The link to the Zoom is available on the Ed Forum (pinned post). Beware that, in the event of a technical failure during the lecture, continuing to accompany the lecture live via zoom might not be possible.

Recording of the lectures will be made available on Mediaspace. We will reuse some of last year's recordings and we may record a few new lectures in case of different lecture contents.

Lecture Schedule

Week	Date	Topic	Suggested Reading	Instructor
Week 1	18 Feb 19 Feb	Introduction \| Building a simple neural classifier [slides] [video] Word embeddings [slides] [video]	Introduction to natural language processing, chapter 3.1 - 3.3 & chapter 14.5 - 14.6 Efficient Estimation of Word Representations in Vector Space GloVe: Global Vectors for Word Representation Enriching word vectors with subword information Advances in pre-training distributed word representations	Antoine Bosselut
Week 2	25 Feb 26 Feb	Classical LMs \| Neural LMs: Fixed Context Models [slides] [video] Neural LMs: RNNs [slides] [video]	Suggested reading: Introduction to natural language processing, chapter 6.1-6.4 A Neural Probabilistic Language Model On the difficulty of training recurrent neural networks Introduction to natural language processing, chapter 3.1 - 3.3 & chapter 18.3, 18.4	Antoine Bosselut
Week 3	4 Mar 5 Mar	Sequence-to-sequence Models \| Transformers [slides] Tokenization [slides]	Suggested reading: Attention Is All You Need The Annotated Transformer The illustrated transformer Between words and characters: A brief history of open-vocabulary modeling and tokenization in NLP Byt5: Towards a token-free future with pre-trained byte-to-byte models Neural machine translation of rare words with subword units	Antoine Bosselut

Exercise Schedule

Week	Release Date	Exercise Session Date	Topic	Instructor
Week 1	19 Feb	26 Feb	Intro + Setup	Madhur Panwar
Week 2	26 Feb	5 Mar	LMs + Neural LMs: fixed-context models Language and Sequence-to-sequence models	Badr AlKhamissi
Week 3	5 Mar	12 Mar	Attention + Transformers + Tokenization	Badr AlKhamissi
Week 4	12 Mar	19 Mar	Pretrained LLMs	Badr AlKhamissi
Week 5	19 Mar	26 Mar	Transfer Learning	Madhur Panwar
Week 6	26 Mar	2 Apr	Text Generation	Madhur Panwar
Week 7	1 Apr	2 Apr	In-context Learning + Post-training	TBD

Contacts

Please email us at nlp-cs552-spring2026-ta-team [at] groupes [dot] epfl [dot] ch for any administrative questions, rather than emailing TAs individually. All course content questions need to be asked via Ed.

Lecturer: Antoine Bosselut

Teaching assistants: Madhur Panwar, Badr AlKhamissi, Zeming (Eric) Chen, Sepideh Mamooler, Ayush Tarun, Lazar Milikic