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Scientific Coordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Angelika Ruf
Tel: +49 221 47694-162

Week 2: A Practical Introduction to Machine Learning in Python

Lecturer(s):
Dr. Damian Trilling, Dr. Anne Kroon

Date: 09.03 - 13.03.2020 ics-file

About the lecturer - Dr. Damian Trilling

About the lecturer - Dr. Anne Kroon

Course description

The course will provide insights into the concepts, challenges and opportunities associated with data so large that traditional research methods (like manual coding) cannot be applied anymore and traditional inferential statistics start to lose their meaning. Participants are introduced to strategies and techniques for capturing and analyzing digital data in communication contexts using Python. The course offers hands-on instructions regarding the several stages of computer-aided content analysis. More in particular, students will be familiarized with preprocessing methods, analysis strategies and the visualization and presentation of findings. The focus will be in particular on Machine Learning techniques to analyze quantitative textual data, amongst which both deductive (e.g., supervised machine learning and inductive (e.g., unsupervised machine learning) approaches will be discussed.
 
To participate in this course, students are expected to be interested in learning how to write own programs where off-the-shelf software is not available.


Target group

Participants will find the course useful if they
  • are social scientists who have the ambition to model quantitative textual data. Specifically, those who aim to describe, explain or predict the content of large-scale textual data using computation techniques are likely to benefit from participating in this course.


Learning objectives

By the end of the course participants will
  • be able to identify research methods from computer science and computational linguistics which can be used for research in the domain of social science;
  • understanding of the principles of supervised and unsupervised machine learning;
  • can explain the principles of these methods and describe the value of these methods;
  • know to analyze textual data;
  • have basic knowledge of the programming language Python and know how to use Python-modules for questions relevant in the domain of the social sciences;
  • be able to independently analyze quantitative textual data using machine learning techniques.


Prerequisites

  • Knowledge of basic statistics (linear and logistic regression)
  • Some basic understanding of programming languages is helpful, but not necessary to enter the course. Students without such knowledge are encouraged to follow the (free) online course at https://www.codecademy.com/learn/python to prepare.


Python, Supervised Machine Learning, Unsupervised Machine Learning


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