ML in Plant Science: Intro to Machine Learning Applications
Learn to apply machine learning techniques in plant sciences, from supervised learning to neural networks, with hands-on Python exercises.
Learn to apply machine learning techniques in plant sciences, from supervised learning to neural networks, with hands-on Python exercises.
This course offers a comprehensive introduction to machine learning applications in plant sciences. It covers key concepts such as supervised learning, test validation, gradient methods, neural networks, regression, and parameter optimization, all within the context of plant biology. Learners will explore real-world examples from scientists currently applying machine learning in plant research. The course features hands-on Python exercises using Jupyter, allowing students to apply their learning to practical plant science questions. By the end of the course, participants will be able to describe fundamental machine learning concepts, implement these approaches in plant sciences, and critically evaluate their implementations. The asynchronous, self-paced format includes self-assessment quizzes for learning reinforcement. This course bridges the gap between machine learning theory and its practical applications in plant biology, making it ideal for those looking to leverage AI in agricultural and botanical research.
Instructors:
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What you'll learn
Describe key concepts in machine learning and their relevance to plant sciences
Identify examples of machine learning applications in current plant science research
Implement supervised learning techniques for plant data analysis
Apply neural networks to solve plant biology problems
Use regression methods for predicting plant traits or behaviors
Optimize parameters in machine learning models for plant science applications
Skills you'll gain
This course includes:
PreRecorded video
Self-assessments, quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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Module Description
This course introduces learners to the applications of machine learning in plant sciences. It covers fundamental machine learning concepts such as supervised learning, test validation, gradient methods, neural networks, regression, and parameter optimization. The course emphasizes practical applications, providing examples of how these techniques are used in plant biology research. Through a series of Python exercises using Jupyter notebooks, students gain hands-on experience in applying machine learning to plant science questions. The curriculum includes case studies from current research, giving students insight into real-world applications. By the end of the course, learners will be able to describe key machine learning concepts, implement these approaches in plant science contexts, and critically evaluate their implementations. The course structure includes self-assessment quizzes to reinforce learning and check understanding.
Fee Structure
Instructors
1 Course
Plant Biologist and Bioinformatics Expert Leading Computational Research at Cornell
Adrian Powell is a distinguished plant biologist and bioinformatics expert currently serving as the Director of the BTI Computational Biology Center (BCBC) at Cornell University. With a Ph.D. in plant biology and a concentration in genomics from Cornell, Powell's research background includes studying nodulation in wild relatives of soybean. In his role at the BCBC, he focuses on providing educational resources and supporting the development of new methods to advance plant life understanding. Powell's expertise spans bioinformatics, genomics, and plant biology, allowing him to address complex biological questions using computational approaches. His work at the intersection of plant science and computational biology has led to the development of innovative courses, such as "Applications of Machine Learning in Plant Science." This course, offered through Cornell University, explores machine learning applications in plant sciences through Python exercises, covering key concepts like supervised learning, neural networks, and regression. The course is designed to provide hands-on experience with real-world examples from plant scientists, bridging the gap between computational techniques and biological research. As an educator and researcher, Powell continues to contribute significantly to the field of plant biology and computational science, fostering a new generation of interdisciplinary scientists
Pioneering Plant Biology and Computational Science Expert
Gaurav Moghe serves as an Assistant Professor in Cornell University's School of Integrative Plant Science, where he leads groundbreaking research at the intersection of plant biology and computational science. His laboratory specializes in studying plant specialized metabolism through an innovative combination of computational biology, molecular biology, biochemistry, and synthetic biology approaches. With 15 years of coding experience, Moghe has established himself as a distinguished researcher in computational metabolomics, comparative genomics, and gene function prediction. His academic contributions include teaching advanced courses such as PLBIO 4000/6000 on Computational Biology, specialized lectures on Mass Spectrometry for metabolomics, and Introduction to Machine Learning for plant science graduate students. His research explores how approximately 300,000 plant species produce over a million different metabolites and their potential applications in agriculture, nutrition, and medicine. Moghe's scientific excellence has been recognized with prestigious honors, including the 2023 Arthur Neish Young Investigator Award from PSNA, and his work has secured significant funding through NSF and USDA grants. His research continues to advance our understanding of plant metabolism while bridging the gap between computational and experimental biology.
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