Principles of data science applied to livestock

Principles of data science applied to livestock

Period
o September 13 – 17, 2021
Instructors
Dr. Guilherme J. M. Rosa
(https://www.gjmrosa.org/): Professor at the Department of Animal & Dairy Sciences and Department of Biostatistics & Medical Informatics, University of Wisconsin Madison

Dr. Joao Dorea
(https://andysci.wisc.edu/directory/joao-ricardo-reboucas-dorea/): Professor at the Department of Animal & Dairy Sciences and Department of Biological Systems Engineering Informatics, University of Wisconsin-Madison.

REGISTRATION

REGISTRATION FEES
250 € for students, PhD students, post-doc etc. and 350 € for researchers and professors (without VAT). To obtain VAT exemption, the public institution must directly make the payment by bank transfer. Details about terms of payment and the potential inclusion (or not) of VAT tax will be communicated once the registration will be done.

Organization / contacts
Please forward your requests of information to Alessio Cecchinato (alessio.cecchinato@unipd.it)

Course Description
The course will cover key concepts and techniques related to statistics and machine learning applied to high-dimensional data in livestock, including data from sensors, imaging, genomics, farm-recorded data from management software, and publicly available datasets. The course is structured with 4 sessions per day, Monday through Friday (Sept 13-17, 2021) – except on Wed afternoon (free time to foster discussion and networking among participants), including expositive lectures and some demo with real data and useful software and algorithms that will be shared with the participants.

Topics
• Basics of Statistics
• Planning Research Studies in Animal Sciences
• Database Management (demo: how to insert, remove, edit data in a SQL)
• Data Analysis Techniques
• Variable Selection and Model Comparison
• Image Processing and Analysis (demo: thresholding, convolution, etc. in Matlab)
• Infrared Spectroscopy and Hyperspectral Imaging (demo: get spectral data from hyperspectral images)
• Wearable Sensing Technology
• Deep Learning
• Genomics Data
• Mining Operational Farm data
• Cloud Computing (demo: Azure Cloud Computing)

Target audience and prerequisites
Graduate level course (PhD and advanced MS) for researchers working in all areas of animal sciences, such as nutrition and physiology, management, genetics and reproduction, in industry or academia. Statisticians, computer scientists, and data scientists interested on learning about potential applications in animal science can also benefit from the course.