CPSC 499

Fall 2025 All Classes

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Advanced Special Topics

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Credit: 1 TO 4 hours.

Advanced experimental course on a special topic in crop sciences.

1 to 4 undergraduate hours. 1 to 4 graduate hours. Approved for Letter and S/U grading. May be repeated if topics vary.

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CPSC 499 class schedule data for fall 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
10564	Independent Study		ARRANGED	n.a.	Location Pending		Part of Term: 1 Date Range: 08/25/25-12/10/25 Special Approval: Instructor Approval Required
79918	Lecture-Discussion	AWM	1:00PM -2:30PM	WF	W115 Turner Hall	Peng, B	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 3 hours Section Title: Agricultural Hydrology Section Info: Junior/Senior-level undergraduate and entry-level graduate course focusing on improving the students’ systems-level understanding of water-agriculture nexus from field to watershed scales under climate change and land use intensification. We will focus on the process understanding of agricultural hydrology (e.g. soil hydrology, soil-plant-atmosphere continuum, watershed hydrology, reactive transport and water quality), practical applications (e.g. drainage management, irrigation management, and conservation management), societal impacts (e.g. food production, nutrient loss reduction, and environmental sustainability), and the start-of-the-art methodology to conduct research related to agricultural hydrology (e.g. field data collection, lab analysis, satellite remote sensing, and numerical modeling, environmental data science, and artificial intelligence).
79927	Lecture-Discussion	IRP	1:30PM -3:20PM	M	M205 Turner Hall	Klimasmith, I	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 2 hours Section Title: Introduction to R Programming
80856	Lecture-Discussion	MDV	4:30PM -6:00PM	TR	337 National Soybean Res Ctr	Ersoz, E	Part of Term: B Date Range: 10/20/25-12/10/25 Credit: 3 hours Section Title: Int Modeling in Ag/BioSci - II Section Info: Integrative Modeling and Data Analytics in Agriculture-II This is a project based experiential learning course- where the trainees are anticipated to learn by doing a semester long Pick-Your-Own-Data Research Project. Instruction covers routinely used multivariate modeling and analytical techniques focusing on life sciences and agricultural applications in an integrated framework of computational, mathematical and statistical modeling. Discussion sessions are leveraged for examining applications from primary literature to understand, evaluate and critique choice of analytical methods used for answering research questions, and provide supplemental lectures as needed. Example topics covered are: Fundamental Multivariate modeling and deep learning with high dimensional data, classic dimension reduction techniques(discriminant analysis, K-means, canonical correlations), simple neural networks and regularization, autoencoders, CNNs, RNNs, foundational AI models and their workings, architectures and applications(LSTM, transformer, GNN, Deep-kernel models), Interpretibility techniques for NNs, ethics, safety and future of AI. Pre-requisite: AP level Programming, Calculus, Basic Statistics, and Fundamentals of Agricultural Science or equivalents or consent of the instructor.
79812	Lecture-Discussion	PBP	1:00PM -2:20PM	TR	M5 Turner Hall	Rai, A	Part of Term: 1 Date Range: 08/25/25-12/10/25 Credit: 4 hours Section Title: Plant Biochemical Pathways Section Info: COURSE TITLE: Plant Biochemical Pathways
79930	Lecture-Discussion	STA	4:30PM -6:00PM	TR	337 National Soybean Res Ctr	Ersoz, E	Part of Term: A Date Range: 08/25/25-10/17/25 Credit: 3 hours Section Title: Int. Modeling in Ag/BioSci- I Section Info: Integrative Modeling and Data Analytics in Agriculture-I This is a project based experiential learning course- where the trainees are anticipated to learn by doing a semester long Pick-Your-Own-Data Research Project. Instruction covers routinely used modeling and analytical techniques focusing on life sciences and agricultural applications in an integrated framework of computational, mathematical and statistical modeling. Discussion sessions are leveraged for examining applications from primary literature to understand, evaluate and critique choice of analytical methods used for answering research questions, and provide supplemental lectures as needed. Example topics Covered are: EDA, Fundamental regression and classification( linear, non-linear, logistic), curve fitting with OLS and MLE, missing data treatments & imputation, goodness-of-fit, regularization, ENSMBL methods, Variance components based modeling (Structural equation Models and Causal Inference) Pre-requisite: AP level Programming, Calculus, Basic Statistics, and Fundamentals of Agricultural Science or equivalents or consent of the instructor.