The course covers major topics in machine learning with applications to animal biosciences and related areas. Topics include: data types, problem types (classification, regression, clustering, dimensionality reduction), models (decision trees, artificial neural networks, k-nearest neighbour, k-means), quality measures (accuracy, precision, recall, errors, correlations), data (re)sampling procedures (k-fold cross validation, fixed percentage splits), and Python implementations using various libraries (pandas, scipy, numpy, scikit-learn). This is a project-based course and it will have a computational component and a lab component focused on Python programming.