For this project, students will review the state of the art in predictive modeling for regression with dependencies that can be represented through graphs. This is not limited to network science models or probabilistic graphical models. The assesment will be reflected on a technical report. Students will then proceed to implement their selected methods and evaluate them on selected datasets


  • Familiarization with adjacent areas to machine learning such as network science, econometrics and stochastic processes
  • Sharpen development skills in Python
  • Implementation in a real world data set

Data sets

  • Private dataset will be shared separately

Target audience

Ideal for students interested in exploring adjacent areas to machine learning or with previous familiarization but interested in applying them to real world problems