ABSTRACT
This chapter introduces the basic concepts of machine learning (ML) in general and deep learning in particular, especially as they relate to computer vision and their applications to plant phenotyping. In short, machine learning is often employed when one needs to generalize from experience in a non-obvious way. In contrast, one would typically not use machine learning in more straightforward tasks like calculating payroll, sorting a list of words, serving web pages, word processing, monitoring CPU usage, and querying a database. In reinforcement learning, the performance of a policy is typically measured by repeatedly using the policy to control an agent in a simulated environment and computing the total reward per episode. In ML, there are many possible sets of functions scriptF that the learning algorithm can choose from, including decision trees, support vector machines, Bayesian networks, and hidden Markov models.
