Life Science Applications of Machine Learning

Catalog description

Machine learning is a fast-developing field of artificial intelligence (AI) with many applications in life sciences. The huge amount of genomic data can be analyzed and interpreted by machine learning techniques. This course introduces basic concepts of machine learning models and demonstrates how these models can solve complex problems in life sciences. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the machine learning toolkits through a tutorial. Main topics cover three branches of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Instead of applying different machine learning methods to different datasets, the course aims to apply different methods to the same datasets so that students are able to compare the performance and pros/cons of the methods. Hands-on exercises will be provided in both lectures and weekly labs. A group project will be given at the end of the semester so that students can apply machine learning methods to the datasets they are interested in. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique to apply for a particular dataset and need, engineer features to meet that need, and write code to carry out an analysis.