This course is a practical introduction to machine learning methods for biological data, focusing on three common data types in biology-images, sequences, and structures. This course will cover how to represent biological data in a manner amenable to machine learning approaches, survey tasks that can be solved with modern deep learning algorithms (e.g. image segmentation, object tracking, sequence classification, protein folding, etc.), explore architectures of deep learning models for each data type, and provide practical guidance for model development. Students will have the opportunity to apply these methods to their own datasets. Not offered 2023-24.