Application of Machine Learning in Agriculture and Livestock Production

Machine learning is the field of study that gives computers the ability to learn from data without being explicitly programmed (Arthur Samuel, AI pioneer, 1959).

Today, applications of machine learning are everywhere and are increasingly growing. Machine learning approaches don’t require any assumption about the distribution of data. They are very robust to missing values and outliers and are performing well in non-linear systems where complex relationships exist between predictor features and the outcome.

In this introductory lecture, we aim to introduce a few machine learning methods and briefly show their applications in agriculture and livestock production.Survey: https://www.surveymonkey.com/r/5PPK9K8

机器学习（Machine Learning）是一门致力于赋予计算机无需显式编程即可从数据中自主学习的研究领域，该定义由人工智能先驱亚瑟·塞缪尔（Arthur Samuel）于1959年提出。
现如今，机器学习的应用场景无处不在且规模持续扩张。机器学习方法无需对数据分布做出任何预设假设，对缺失值与异常值具备极强的鲁棒性，且在预测特征与输出结果间存在复杂关联的非线性系统中表现优异。
在本次入门讲座中，我们旨在介绍若干机器学习方法，并简要展示其在农牧业生产中的应用。调查问卷链接：https://www.surveymonkey.com/r/5PPK9K8