MSN 511: Collective Intelligence; Definitions, Measurement, Experimentation

MSN 511: Collective Intelligence; Definitions, Measurement, Experimentation
Yiannis Laouris, MD, PhD

Society faces such complexity today that makes it obligatory to combine the efforts and the intelligence of not just many people, but also the efforts and intelligences of humans and machines in order to respond to the challenges. In this course students will familiarize themselves with the definitions and the challenges of measuring collective intelligence.

The topics will include recommendation systems, clustering, ranking, optimization, classifiers, decision trees, k-nearest neighbors, kernel methods and support vector machines, feature extraction and genetic programming. The course will also consider how the business of the Web has adapted to take advantage of collective intelligence.

The course will include coursework and a major experimental project. Students will explore the emerging science of collective intelligence, through opportunities to interact with concrete already programmed examples.

The grade will be based on completing assignments and class participation.

Syllabus Reading Resources:

Benkler, Y., & Masum, H. (2008). Collective intelligence: creating a prosperous world at peace. Oakton: Earth Intelligence Network, 2008.

Smith, H.M. (2013). 9 Design Principles for Collective Intelligence and Prosperity: A Systems Framework for Scaling from Individual to Global (ed. 2). United States of America: EnneaGlobal.