Machine Learning has a wide range of applications in marketing, finance, life sciences, healthcare, as well as technology. This course is designed to provide a practical understanding of key concepts in Machine Learning and to develop hands-on experience in building machine learning models with real data from various sources. Topics covered include methods of data gathering, data processing, data exploration, visualization, classification, regression, and network analysis. Students will gain a deeper understanding by working with data using open source tools, such as R, R Studio, and Cytoscape. Application areas will emphasize life sciences and biotechnology.
Undergraduate courses on probability and statistics, or instructor consent; successful completion of ALS 342 (or placement waiver) and ALS 411.