Scientific Coordination
Dr.
Marlene Mauk
Tel: +49 221 47694-579
Marlene Mauk
Tel: +49 221 47694-579
Administrative Coordination
Noemi Hartung
Tel: +49 621 1246-211
Tel: +49 621 1246-211
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Automated Image and Video Analysis with Python
About
Location:
Mannheim B6, 4-5
Mannheim B6, 4-5
Course duration:
09:00-16:00 CEST
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Andreu Casas, Felicia Loecherbach
Course description
Social scientists have long argued that images play a crucial role in shaping and reflecting political life. This role is heightened by the bombardment of images that people experience today through many communications channels, from television to social media. Digitization has both increased the presence of images in daily life and made it easier for scholars to access and collect large quantities of pictures. However, using images collected in observational settings as data for social science inference is an arduous task. Fortunately, recent innovations in computer vision, the subfield of computer science concerned with automated image analysis, can reduce the costs of using images as data.
In this course, we'll dig into the necessary theoretical and methodological expertise needed to apply machine learning methods to address social science questions. We will combine theoretical sessions where we'll discuss research using computer vision methods for the study of politics, communication science, etc.; with sessions where we'll cover in details key methodological advances needed to fully understand state-of-the-art computer vision methods (deep learning, neural networks, convolutional neural networks, etc.), as well as practical sessions where we'll go over several python tutorials implementing different computer vision techniques, for image processing (e.g. splitting videos into analytical frames), object and face detection, image (supervised and unsupervised) classification, facial trait analysis, etc.
Students with basic programming skills/experience in Python and some machine learning background will get the most out of the course. However, we'll also take the time to briefly review some key machine learning concepts necessary to implement machine learning methods, and students will be provided with clear and easy-to-follow sample code for each of the practical tutorials. By the end of the course, students will have a good understanding of the kind of research questions that can be answered using computer vision methods, as well as a good understanding of several techniques and how to apply them for their own research.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
Target group
Participants will find the course useful if:
Learning objectives
By the end of the course participants will:
Organisational Structure of the Course
The course will be organized around three different types of sessions:
The lectures will take place in the morning of the first three days (Monday-Wednesdays). Tutorials will take place in the afternoon of the first three days, as well as at different times during the last two days. The consulting sessions will also take place at different times during the last two days.
Prerequisites
For those who would like a primer or refresher in Python, we recommend taking the online workshop “Introduction to Python” that takes place from 04-06 September 2023.
Software and hardware requirements
Participants should bring their own laptop for use in the course.
The course will use Google Colab, so participants need a Google account. There is no need to install Python locally.
Agenda
Monday, 18.09. | |
Morning Session | |
Afternoon Session | |
Tuesday, 19.09. | |
Morning Session | |
Afternoon Session | |
Wednesday, 20.09. | |
Morning Session | |
Afternoon Session | |
Thursday, 21.09. | |
Morning Session | |
Afternoon Session | |
Friday, 22.09. | |
Morning Session | |
Afternoon Session |