Statistical Learning with Applications in R
7.5 ECTS creditsStudents acquire knowledge of modern methods of statistical learning.
Supervised methods of learning:
- regression methods
- classification methods
- support vector machine
Unsupervised methods of learning:
- principal component analysis
- k-means clustering and hierarchical clustering
R commands
Supervised methods of learning:
- regression methods
- classification methods
- support vector machine
Unsupervised methods of learning:
- principal component analysis
- k-means clustering and hierarchical clustering
R commands
Progressive specialisation:
G2F (has at least 60 credits in first‐cycle course/s as entry requirements)
Education level:
Undergraduate level
Admission requirements:
60 ECTS credits completed in Statistics
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.