Artificial Intelligence (AI) has enormous potential for animal disease prevention and timely control. Although early research on AI applied to animal health is promising, there are still several gaps to be filled. Much of this research has been conducted by experts in data analysis but often with little knowledge of animal health and animal production. AI research in animal health and production requires domain, coding, and statistical skills. This course will introduce these three skills to train students as the next generation of animal data scientists, an unmet need for the animal health and production sector.
Objectives
- Introduce programming languages (R and Python) to manage and analyze data
- Introduce elements of animal data management and exploratory data analysis
- Introduce artificial intelligence tools that may help improve animal health and production
Description:
This course will integrate the foundations of data science (coding, statistics, and domain expertise) with application examples in animal health. This course is for graduate students with an interest in applying advanced analytic techniques to animal health-related data, from data visualization to modeling, including prediction models, using popular machine learning.
Students must have completed elemental statistical courses.
Following the course, students will be able to understand how to use, analyze, and interpret data related to animal health.
The course has one credit (15 hours of presential workload), composed of 7 sections of 1 hour each, followed by 1 hour of practical exercises. For every other section, students will need to submit homework based on the topics covered in previous classes. The course will end with a final exam (1 hr).
The course is led by Pablo Valdes Donoso (pablo.valdes.donoso@umontreal.ca), Assistant Professor of AI in Veterinary Medicine and Director of the Plateforme IA-Agrosanté, at the Faculty of Veterinary Medicine of the Université de Montréal.