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/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.
The course introduces machine learning and data mining and will enable the student to tackle a wide range of data science problems. Four topics are covered in machine learning: • concept of model, simple models (Dts, kNN...), overfitting, model selection • supervised learning (linear models, ensembles, neural networks, SVMs...) • unsupervised learning (clustering, visualisation, density estimation...) • probabilistic learning (probabilistic inference and probabilistic models)
The course is divided into two learning activities. The first is the lectures and is assessed by an oral examination on the theory of the course. The second is a continuous assessment of the students' ability to apply the techniques seen in the course. This consists of solving simple data analysis problems. The examination and the continuous assessment count for 80% and 20% of the course grade respectively.