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AI Foundry for Agricultural Applications Summer School

Overview: 

Increase competency in solving agricultural issues with artificial intelligence during this week-long hands-on course designed for graduate students with limited experience in machine learning. 

Participants in the AI Foundry for Agricultural Applications summer school will participate in four days of lectures and virtual activities on topics focused on AI and machine learning in agriculture applications. Students will be mentored by faculty from the Departments of Agricultural and Biological Engineering, Animal Sciences, and Industry partners. The program will teach skills applicable to many agricultural applications, and the data sets utilized during the course will be specific to livestock systems. On the last two days of the course, participants will be challenged to develop a solution to a digital agriculture problem in an inspiring Hackathon. All events will be virtual.

Participants can expect to complete the course with an increased ability to engage in conversations and idea-generation for AI applications, as well as implementing existing learning models in basic applications. 

Registration: 

We encourage graduate students and postdocs from any major interested in agricultural applications to apply to attend the summer school. The summer school is being offered at no cost to students, but we request registration by July 1st. Register for the class by filling out this form. 

Students should have some background in coding (coding logic and basic pseudocode) using any language. The course will use Python, and students who do not know Python MUST complete this free self-guided training prior to the summer school to be prepared to be successful in summer school. 

If you have any questions, please contact Christina Tucker lyvers2@illinois.edu

Schedule: 

The summer school will be hosted virtually July 11-16th from 9am-4:30pm CST daily with a scheduled break from 12-1pm and shorter breaks intermittently. 

Topics We’ll Explore: 

  • Foundations of machine learning and artificial intelligence 
  • Introduction to digital technology in animal systems
  • Deep learning overview
  • Object identification and tracking models 
  • Working with datasets
  • Digital tools for managing animals
  • Challenges of technology deployment in animal systems
  • Safety and human impact of AI
  • … and more!