This course provides a comprehensive, practice-focused overview of machine learning (ML) for modern sensor-based agriculture. Students will learn to develop computational pipelines for predictive modeling of agricultural data, covering data collection, cleaning, feature engineering, model selection, and performance evaluation. While some familiarity and interest in programming is recommended, the course begins with a condensed introduction to Python before progressing into more advanced methods. Throughout the semester, students will build a strong foundation in ML theory and gain extensive hands-on experience through coding exercises and real-world datasets, ultimately preparing them to tackle current challenges in agricultural analytics.

Offered every Fall semester.

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This course introduces students to precision animal farming, focusing on the latest technologies used to monitor animal health, behavior, and performance, both in academic and commercial settings. Students will explore phenotyping techniques, wearable sensors, computer vision, drones, and Artificial Intelligence (AI) applications. You will understand the role AI plays in agriculture, and evaluate the opportunities and challenges of these technologies in real-world farming practices.

The course is offered every Spring semester.

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