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Enseignement scientifique & technique - SD-TSIA214 : Machine Learning for Text Mining (option apprentissage statistique)

Domaine > Mathématiques.

Descriptif

Cours en anglais

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.

Content
During this course, the students will acquire theoretical and technical skill on advanced machine learning methods and natural language processing. 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.

Prerequisites
Students are supposed to have followed SD205 (advanced statistics).

Teaching
The course will alternate lectures and lab work sessions.

Evaluation : Lab report and final exam.

Pour les étudiants du diplôme Diplôme d'ingénieur

Students are supposed to have followed SD205 (advanced statistics).

Format des notes

Numérique sur 20

Littérale/grade européen

Pour 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 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.

Programme détaillé

 

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