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| FNR 57400 - Big Data, AI, And Forests |
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Credit Hours: 3.00. 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.
3.000 Credit hours Syllabus Available Levels: Undergraduate, Graduate, Professional Schedule Types: Distance Learning, Lecture Offered By: College of Agriculture Department: Forestry and Natural Resources Course Attributes: Upper Division May be offered at any of the following campuses: West Lafayette Learning Outcomes: 1. Equip 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. 2. Equip themselves with general problem-solving skills to overcome practical big-data challenges. 3. Equip themselves with a synthetic understanding of the strength and weakness of various big data tools and machine learning algorithms. Restrictions: May not be enrolled as the following Classifications: Freshman: 15 - 29 hours Sophomore: 45 - 59 hours Freshman: 0 - 14 hours Sophomore: 30 - 44 hours |
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