Technical Electives

The technical electives list for the Predictive Plant Phenomics (P3) program degree specialization continues to grow as more courses are introduced. Please revisit this list as courses may be added each semester. If a P3 program trainee or P3 faculty affiliate believes a course is relevant to the P3 curriculum, please contact Nicole Scott, P3 program coordinator, with the course details.

Engineering Technical Electives Examples

Fall

 Integrated Transport Phenomena (CH E 554)
 Transport Phenomena I (CH E 356)
 Instrumentation for Ag & Biosystems Engineering (A B E 504)
*Biosensors (B M E 450)
 Electronics, Microelectronics, & Photonics (E E 530)
 Fundamentals of Remote Sensing (C R P 454/554)

Spring

 Advanced Machine Design (M E 517)
 Modeling and Simulation (M E 475)
*Data Analytics and Machine Learning for Cyber-Physical Systems Applications (M E 592X)
*Advanced Topics (e.g., Visual Sensing and Sensemaking) (A B E 690)
 Machine Vision (M E 556)
 Machine Learning (COM S 573)

   
Plant Sciences Technical Elective Examples

Fall

 Plant Molecular, Cell and Developmental Biology (GDCB 545)
*Plant Metabolism (GDCB/PLBIO 513)
 Crop Genetics (AGRON 506)
 Molecular Biology of Plant-Pathogen Interactions (PL P 692)
*Population & Quantitative Genetics for Breeding (AGRON 561/An S 561)
*Principles of Cultivar Development (AGRON 521)
 Applied Plant Molecular Genetics & Biotechnology (AGRON 524)
 Introduction to Plant Breeding (AGRON 421)
*Transmission Genetics (GDCB 510)
 Molecular Virology (PL P 608)
 Principles of Plant Pathology (Pl P 508)

Spring

*Molecular Genetics (GDCB 511)
 Global Change (AGRON 504)
 Bacterial-Plant Interactions (PL P 477)
 Crop Physiology (AGRON 516)
 Quantitative Genetics for Plant Breeding (AGRON 528)
 Principles of Plant Pathology (Pl P 508)
 Plant Virology (Pl P 509)

   
Data Sciences Technical Elective Examples

Fall

 Software Tools for Large Scale Data Analysis (CPR E 419)
 Pattern Recognition (E E 547)
 Principles of Artificial Intelligence (COM S 572)
 Algorithms for Large Data Sets: Theory & Practice (COM S 535)
 High Performance Computing for Science & Engineering Applications (CPR E 425)
*Bioinformatics I: Bioinformatics Algorithms (BCB 567)
 Fundamentals of Remote Sensing (C R P 454)
 GIS Programming and Automation (C R P 456)
 Fundamentals of Bioinformatics (GDCB 544)
*Crop and Soil Modeling (AGRON 525)
Big Data Analytics & Optimization (IE 487/587)
Data Science & Analytics for Agricultural and Biosystem Engineers (ABE 516X)
*Computational Skills for Biological Data (EEOB/BCB 546X) (pdf)

Spring

 Bioinformatics IV: Systems Biology (BCB 570)
*Bioinformatics II: Statistical Bioinformatics (BCB 568)
*Bioinformatics I: Bioinformatics Algorithms (BCB 567)
 Evolutionary and Ecological Genomics (EEOB 561)
 Machine Learning (COM S 573)
 Applied Modern Multivariate Statistical Learning (STAT 502)
 Exploratory Methods and Data Mining (STAT 503)
 Statistical Methods for Spatial Data (STAT 406)
 Fundamentals of Systems Biology and Network Science (BCBIO 402)
*Data Analytics and Machine Learning for Cyber-Physical Systems Applications (M E 592X)

* Courses marked with an asterisk are recommended by P3 students and/or faculty