Computing and Modeling for Scientific Cultural Research

Advances in modeling techniques and information science provide an opportunity to understand culture in novel ways. “Culturoinformatics” is a field of science that leverages various cultural data (aural, graphical, or textual) and scientific tools to model and appreciate the creation, delivery, and consumption processes in culture. Examples include use of colors in paintings,  the social networks of collaboration between artists, the evolution of memes in authorships, etc. Designed for graduate students of Culture Technology at KAIST and others interested in interdisciplinary science, we will cover the fundamentals of data science, both the analytical and computational aspects . Students will be required to conduct individual term projects, and will have a chance to interact with the lecturer.

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Music as a series of symbols. Image from http://j.mp/2ncxcEu


Contents

  • Culture and Science: Art and Culture Through the Lens of Science
  • Fundamentals
    • The cyle of observation, modeling, and prediction
    • Large many-body systems and emergence
  • Relational Data: Network Science
    • What is a Network?
    • Network Representation, Methods, and Modeling
  • Data and Statistics
    • What is a statistic?
    • Component Analysis – Eigensystems, Fourier Transform, and Multi-Dimensional Scaling
  • Information Visualization: Algorithms and Methods
    • Algorithmic Approach: Network Visualization
  • Miscellaneous Topics in Culturoinformatics
    • Language
    • Music & Art
    • Science & Technology

On Projects

  • Project timelines
    • Presentation of topics and problems (Apr 3 and Apr 5)
    • Preliminary presentation (May)
    • Final presentation (June)
  • Projects can be on an interesting problem regarding an interesting cultural system (pictures, music, exhibitions, etc.). Past project themes include
    • Preferences between cuisines
    • The sentimentality in novels
    • Use of color in western paintings
    • Player performance in sports
    • Possible biases in coaches’ selection of players
    • Communities in narratives
    • Clustering hotspots in taxi services
    • Memes spreading on flickr

More Information

  • Venue: Nam June Paik Hall at N25.
  • Class Hours: Mon, Wed 14:30-15:45
    • Office Hours: by appointment with me at N25-3230
  • Graduate Student Instructor: DonghyeokChoi

Class Contents (To Be Updated)

  1. Culture and Science
  2. Introduction, Scientific Methods
  3. Scientific Methods
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