Learning outcomes

At the end of the course, the student should be able to demonstrate an understanding of the different topics covered (see content), i.e. be able to express in his or her own words the theory and methods seen in the course and explain in what context they are useful. The student must also be able to apply the techniques seen in the course to simple data analysis problems and to document a current issue in machine learning and data mining.

Content

The course introduces machine learning and data mining and will enable the student to tackle a wide range of data science problems. The following topics will be covered:

  • concept of model, simple models (decision trees, kNN...), overfitting, model selection
  • supervised learning (linear models, ensembles, neural networks...)
  • unsupervised learning (clustering, visualisation..)
  • use of machine learning in the real world

Assessment method

The course is divided into two learning activities. The first is the lectures and is assessed by an oral exam on the theory of the course. The second is a continuous assessment of the students' ability to implement the techniques seen in the course to solve simple data analysis problems. The examination and continuous assessment count for 70% and 30% of the course grade respectively.

Sources, references and any support material

References are given during the course.

Language of instruction

Anglais