CS-727 / 2 credits

Teacher: West Robert

Language: English

Remark: Next time: Spring 2022


Frequency

Every year

Summary

This is a seminar course. By reading and discussing an introductory book as well as research papers about computational social science, students will become familiar with core issues and techniques in the field.

Content

Keywords

Computational social science, social networks, text analysis, natural language processing, information dynamics, machine learning

Learning Prerequisites

Required courses

No formal prerequisites, but we expect students to have a basic understanding of statistics, probabilities, and machine learning

Learning Outcomes

By the end of the course, the student must be able to:

  • Critique scientific papers
  • Present other scholar's work
  • Assess / Evaluate positive aspects of given scientific papers
  • Identify negative aspects of given scientific papers

Resources

Bibliography

Previous editions: https://dlab.epfl.ch/teaching/

Introductory book read as part of the class: http://www.bitbybitbook.com/

Ressources en bibliothèque

In the programs

  • Exam form: Multiple (session free)
  • Subject examined: Topics in Computational Social Science (TopiCSS)
  • Lecture: 28 Hour(s)

Reference week