Descriptif
Cours en anglais
This course is designed for students who will be attending classes and labs.
This course is designed for students who will be attending classes and labs.
Objectives
Text mining is a progressing and challenging domain. For example, a lot of efforts have been recently dedicated to the development of methods able to analyze opinion data available on the social Web. The first objective of this course is to tackle the different methods of language processing and machine learning underlying text and opinion mining.
Objectifs pédagogiques
During this course, the students will acquire theoretical and technical skill on advanced machine learning methods and natural language processing. This course is designed for students who will be attending classes and labs.
24 heures en présentiel (16 blocs ou créneaux)
Diplôme(s) concerné(s)
Parcours de rattachement
Pour les étudiants du diplôme Echange international non diplomant
Machine learning (theoretical foundations) and basis of neural networks
Pour les étudiants du diplôme Diplôme d'ingénieur
Students are supposed to have followed SD-TSIA 210 Machine learning
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Diplôme d'ingénieur
Vos modalités d'acquisition :
Evaluation : Lab report and final exam.
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
- Crédit d'UE électives acquis : 2.5
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Echange international non diplomant
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Programme de mobilité des établissements français partenaires
L'UE est acquise si Note finale >= 10- Crédits ECTS acquis : 2.5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Programme détaillé
The techniques and concepts that will be studied include:
-natural language pre-processing : tokenization, part-of-speech tagging, document representation and word embeddings techniques
-natural language resources : lexicons, wordnet and framenet
-text clustering and text categorization : advanced machine learning methods such as deep learning, non-negative matrix factorization, hidden markov models, etc.