Instructor: Dr. Theodora Chaspari
This course covers hands-on applications of artificial intelligence for social good. The course will go over fundamental methods, algorithms, and systems related to data science and machine learning. It will further examine how AI algorithms can be applied to well-being, health, education, and safety applications for promoting social good. The class draws inspiration from and is coordinated with the Envisioning the Neo-traditional Development by Embracing the Autonomous Vehicles Realm (ENDEAVR) project aiming to improve small communities by enabling them to become smart cities and enhance their capacity to utilize emerging technologies to accomplish desired socio-economic, environmental, and health outcomes. The course further aims to provide an interdisciplinary experience to students using smart city applications as a testbed. Students will work on their own discipline, but also in collaboration to the other disciplines, including Urban Planning, Landscape Architecture, and Visualization, toward a final project that is inspired by community needs. Nolanville, TX, will be used as a testbed and students will be called to solve real-life problems inspired by the needs of Nolanville, including health, transportation, and well-being, in collaboration with their interdisciplinary team. Students will have weekly meetings with students from the other and will communicate with representatives from Nolanville, TX, to better understand the community needs and accomplish their final project.
No pre-requisites are stated. An understanding of machine learning is recommended for project purposes.
- Be able to formulate a machine learning problem inspired by real-life needs.
- Be able to analyze data in relation to well-being, safety, education, and health outcomes.
- Be able to design computational models for benefiting smart cities applications.
- Be able to design projects that generate new findings and algorithmic contributions to the fields of smart city analytics.
- Be able to critically analyze state-of-the-art research papers including the experimental design, methodology, technical approach, and system.
- Be able to interact, communicate, and work together with students from other disciplines in order to identify data analytics and machine learning solutions to real-life problems.