CS-552 / 6 crédits

Enseignant: Bosselut Antoine

Langue: Anglais


Summary

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.

Content

Learning Prerequisites

Recommended courses

  • CS-233a or CS-233b Introduction to machine learning
  • CS-433 Machine learning

 

Important concepts to start the course

- Python programming
- Probability and Statistics
- Linear Algebra
- Machine Learning concepts

Learning Outcomes

By the end of the course, the student must be able to:

  • Define basic problems and tasks in natural language processing (e.g., machine translation, summarization, text classification, language generation, sequence labeling, informaiton extraction, question answering)
  • Implement common modern approaches for tackling NLP problems and tasks (embeddings, recurrent neural models, attentive neural models) and how to train them
  • Understand failure modes of these models and learning algorithms (e.g., robustness, interpretability/explainability, bias, evaluation)
  • Review academic research papers and understand their contributions, strengths, and weaknesses according to the principles learned in lecture
  • Complete a project that applies these algorithms to a real-world NLP problem, where they will define a task, evaluation, model implementation, and analyze the shortcomings of their approach

Teaching methods

  • Lectures
  • Lab sessions
  • Paper reading
  • Course project

Expected student activities

  • Attend lectures and participate in class
  • Complete homework assignments
  • Complete a review of a research paper of their choosing published at an NLP conference over the last 5 years
  • Complete a project of their choosing (agreed upon with course supervisor) : complete a project proposal outlining topic and evaluation plan; submit two project milestones; submit final project report; present project findings to committee of course instructor and TAs.

Assessment methods

  • Assignments, including paper review (40%)
  • Group Project (60%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Dans les plans d'études

  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines
  • Semestre: Printemps
  • Forme de l'examen: Pendant le semestre (session d'été)
  • Matière examinée: Modern natural language processing
  • Cours: 3 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Projet: 1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22