This workshop is designed to introduce life scientists with some programming experience to machine learning methods. By the end of the course you should be able to: 1) identify the types of problems that machine learning methods can be applied to, 2) have an understanding of basic concepts like the bias-variance tradeoff, and test/train/validate, 3) have experience applying a few types of classification and regression methods, 4) have a general sense of the methods and software available, and 5) know where to go to continue learning about ML after you leave.
The course has been jointly developed by the USDA Agricultural Research Service and the University of Florida Carpentries Club. The course is designed to be very interactive so we are using Jupyter notebooks for our live coding lectures and for the in class exercises that follow each lesson. We will be using the Python programming language and the Scikit-Learn machine learning library for the course. We will go over Python basics so that people family with scripting or programming in R, Perl Bash or other languages can actively participate.