(IT 534)
| Nom du Cours | Semestre du Cours | Cours Théoriques | Travaux Dirigés (TD) | Travaux Pratiques (TP) | Crédit du Cours | ECTS | |
|---|---|---|---|---|---|---|---|
| IT 534 | 3 | 4 | 0 | 0 | 3 | 8 |
| Cours Pré-Requis | |
| Conditions d'Admission au Cours |
| Langue du Cours | Anglais |
| Type de Cours | Obligatoire |
| Niveau du Cours | Master |
| Enseignant(s) du Cours | İsmail Burak PARLAK bparlak@gsu.edu.tr (Email) |
| Assistant(e)s du Cours | |
| 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 . |
| Contenus |
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 |
| Acquis d'Apprentissage du Cours | |
| Méthodes d'Enseignement | |
| 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 |
Intitulés des Sujets Théoriques
| Semaine | Intitulés des Sujets |
|---|
Intitulés des Sujets Pratiques
| Semaine | Intitulés des Sujets |
|---|
Contribution à la Note Finale
| Numéro | Frais de Scolarité | |
|---|---|---|
| Contribution du contrôle continu à la note finale | 1 | 50 |
| Contribution de l'examen final à la note finale | 1 | 50 |
| Toplam | 2 | 100 |
Contrôle Continu
| Numéro | Frais de Scolarité | |
|---|---|---|
| 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 |
| Toplam | 3 | 50 |
| No | Objectifs Pédagogiques du Programme | Contribiton | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activités | Nombre | Durée | Charge totale de Travail |
|---|---|---|---|
| Charge totale de Travail | 0 | ||
| Charge totale de Travail / 25 | 0,00 | ||
| Crédits ECTS | 0 | ||