Master TAL - MSc. NLP

Course Unit

Machine learning

UE

801

EC

EC1

Hours

30h

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

 

Prerequisites

  • UE 701

Targeted Skills

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

Professor

Marianne Clauzel

IDMC

More Informations

Bibliography

  • 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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