Project Description

Icon_MiningWith the advent of computers and the information age, vast amounts of data are being generated in many fields, and both scientists as well as business majors need to understand how to learn from data.

The learning problems that the course considers can be roughly categorized as either supervised or unsupervised. In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures; in unsupervised learning, there is no outcome measure, and the goal is to describe the associations and patterns among a set of input measures. While some mathematical details are needed, the course emphasizes the methods and their conceptual underpinnings rather than their theoretical properties.

  • Credits: 6,5 ECTS
  • Total workload: 195 hours
  • Contact hours: 4 hours per week
  • Language of instruction: english
  • Form of examination: written and oral
  • Weight for the final grade: 7/84