3 credits
Spring 2026 Lecture Upper Division
This course is focused on introductory big data analysis, artificial intelligence, and associated applications in large-scale forest research. The lecture will cover the challenges we encounter in big data ecological research, and the approaches to overcome these challenges. Real-time forest inventory and wildlife survey data at national and continental levels will be utilized in this course, and actual high-impact research projects will be introduced as case studies to inform students of the state-of-the-art in this subject area. High-performance computing clusters will be utilized for big data analysis. This course is also open to non-forestry majors. We will introduce basic machine learning techniques that are applicable to other subject areas. Guest lectures may cover big data analyses in different fields, internet-of-things, and/or data management and optimization/decimation for collaborative Virtual Reality experiences. The class will be evaluated through a final project, for which students will work independently or in a group setting to develop a 'mini' research manuscript with a title of their own selection. All the groups are encouraged to submit their manuscript for publication at peer-reviewed journals, and those whose manuscripts have passed the initial journal screening will get extra bonus points.
Learning Outcomes1Equip themselves with critical thinking skills to evaluate big-data research topics and their potential alignment with top-tier journals such as Science, Nature, and PNAS.
2Equip themselves with general problem-solving skills to overcome practical big-data challenges.
3Equip themselves with a synthetic understanding of the strength and weakness of various big data tools and machine learning algorithms.
Restrictions
made by Purdue students.