Machine learning and data mining [IDASM102]

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