Antoine Bosselut

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antoine.bosselut@epfl.ch +41 21 693 62 13 https://atcbosselut.github.io

EPFL IC IINFCOM NLP
INR 234 (Bâtiment INR)
Station 14
1015 Lausanne

+41 21 693 62 13
Office:  INR 234
EPFL > IC > IINFCOM > NLP

EPFL IC-DO
INR 234 (Bâtiment INR )
Station 14
1015 Lausanne

+41 21 693 62 13
Office:  INR 234
EPFL > IC > IC-SIN > SIN-ENS

Web site:  Web site:  https://sin.epfl.ch

EPFL IC-DO
INR 234 (Bâtiment INR )
Station 14
1015 Lausanne

+41 21 693 62 13
Office:  INR 234
EPFL > IC > IC-SSC > SSC-ENS

Web site:  Web site:  https://ssc.epfl.ch

EPFL IC-DO
INR 234 (Bâtiment INR )
Station 14
1015 Lausanne

+41 21 693 62 13
Office:  INR 234
EPFL > VPA-AVP-PGE > AVP-PGE-EDOC > EDIC-ENS

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Administrative data

Fields of expertise

Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML)

Teaching & PhD

Teaching

Computer Science

Communication Systems

PhD Students

Bayazit Deniz, Borges Ribeiro Beatriz Maria, Chen Zeming, Cui Shaobo, Foroutan Eghlidi Negar, Gao Silin, Ismayilzada Mahammad, Mamooler Sepideh, Mi Li, Petersen Molly Rose,

Courses

Introduction to natural language processing

The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

Modern natural language processing

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, students will learn algorithmic tools for tackling problems in modern NLP.

Topics in Natural Language Processing

This seminar course explores advanced topics in natural language processing through a mix of reading, reviewing, and writing academic papers.

EECS Seminar: Advanced Topics in Machine Learning

Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoretical and experimental results.