Master TAL - MSc. NLP

Course Unit

Machine learning







Course Description

This course aims at presenting fundamental principles of Machine Learning. We will first introduce the different types of learning a student may be confronted with in industry : supervised, semi-supervised, and unsupervised learning. We then give some basis about numerical optimization. An important part of the course is devoted to good practices in machine learning, including : analysis, preprocessing and visualization of data, evaluation with a special emphazis on the choice of the metric. The applicative part will include analysis of large corpora.


Learning Outcome

  • Ability to analyze, visualize  a dataset
  • Basics in optimization methods used in Machine learning (SGD, Adams)
  • Overview of classical approaches in machine learning
  •  Evaluation and main metrics
  • Practical sessions in Python with the Jupyter environement



  • UE 701

Targeted Skills

  • Develop an argument with critical thinking skills
  • Combine interdisciplinary skills and know-how in the aims of creating innovative solutions


Marianne Clauzel


More Informations


  • To be completed

Course URL – Arche

  • To be completed

Link with other courses

  • to be completed

Evaluation procedures

Number of Tests

  • One final test

Nature of the tests

  • Final Exam

Combine with other specialization

  • MSc Cognitive Science

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