FICHE DESCRIPTIVE DU COURS · IT 534

Obligatoire · Anglais
ECTS
8
Crédit local
3
Cours Théoriques + Travaux Dirigés (TD) + Travaux Pratiques (TP)
4 + 0 + 0
Niveau du Cours
Master
Cours Pré-Requis
-
Semestre du Cours
3

Contenus

Objectif du Cours

Introduce current aspects of the design and the implementation of computing systems that can process, understand, or communicate in human language. The course covers fundamental approaches, largely machine learning and deep learning, used across the field of NLP as well as a comprehensive set of NLP tasks both historical and contemporary. Problems range from syntax (part-of-speech tagging, parsing) to semantics (lexical semantics, question answering, grounding) and include various applications such as summarization, machine translation, information extraction, and dialogue systems. Assignments throughout the semester involve building scalable machine learning systems for various NLP tasks.
Suggested Background:
Data Structures and Algorithms, Linear Algebra, Introduction to Artificial Intelligence-Machine Learning

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Contenu du Cours

Week 1: Introduction to NLP, Regex, Finite State Machines, Edit Distance
Week 2: Finite State Transducers, Text Normalization,
Week 3: Language models, tf-idf, bag of words, n-grams
Week 4: Lexical, syntactic and morphological analysis
Week 5: Semantic analysis
Week 6: Text classification, text summarization
Week 7: Machine translation, Q&A Systems, Chatbots
Week 8: Speech Analysis
Week 9: Neural Nets, Embeddings
Week 10: Deep Learning and Language Models
Week 11: Projects

Ressources

1- Speech and Language Processing, D. Jurafsky& J.H. Martin, https://web.stanford.edu/~jurafsky/slp3/ 3rd edition draft
2- Foundation of Statistical Natural Language Processing, C.D. Manning & H. Schütze, MIT Press, 2003
3- Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper O’Reilly, 2009: http://www.nltk.org/book/
Supplementary Books:
4- Python 3 Text Processing with NLTK 3 Cookbook, Jacob Perkins, Packt Publishing, 2014
5- Applied Text Analysis with Python, Benjamin Bengfort, Tony Ojeda, Rebecca Bilbro, O’Reilly, 2018
6- Turkish Natural Language Processing, Kemal Oflazer, Murat Saraçlar, Springer, 2018
7- Neural Network Methods for Natural Language Processing, Yoav Goldberg, Morgan & Claypool, 2017

Système d'évalution

Contribution à la Note Finale
Activités Numéro Contribution
Contribution du contrôle continu à la note finale 1 50
Contribution de l'examen final à la note finale 1 50
Total 2 100
Contrôle Continu
Activités Numéro Contribution
Devoir 2 25
Présentation 0 0
Examen partiel (temps de préparation inclu) 0 0
Projet 1 25
Travail de laboratoire 0 0
Autres travaux pratiques 0 0
Quiz 0 0
Devoir/projet de session 0 0
Portefeuille 0 0
Rapport 0 0
Journal d'apprentissage 0 0
Mémoire/projet de fin d'études 0 0
Séminaire 0 0
Autre 0 0
Total 3 50

Evalution de la Compétence

No Objectifs Pédagogiques du Programme Contribiton
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X
9 X
10 X
11 X

Tableau de la Charge de Travail

Activités Nombre Durée Charge totale de Travail
Durée du cours 0 0 0
Préparation pour le cours 0 0 0
Devoir 0 0 0
Présentation 0 0 0
Examen partiel (temps de préparation inclu) 0 0 0
Projet 0 0 0
Laboratoire 0 0 0
Autres travaux pratiques 0 0 0
Examen final (temps de préparation inclu) 0 0 0
Quiz 0 0 0
Devoir/projet de session 0 0 0
Portefeuille 0 0 0
Rapport 0 0 0
Journal d'apprentissage 0 0 0
Mémoire/projet de fin d'études 0 0 0
Séminaire 0 0 0
Autre 0 0 0
baclé 0 0 0
Charge totale de Travail 0
Charge totale de Travail / 25 0.00
Crédits ECTS 0