This course introduces students to the field of machine learning with emphasis on the probabilistic models that dominate contemporary applications. Students will discover how computers can learn from examples and extract salient patterns hidden in large data sets. The course will introduce classification algorithms that predict discrete states for variables as well as regression algorithms that predict continuous values for variables. Attention will be given to both supervised and unsupervised settings in which (respectively) labeled training data is or is not available. Emphasis is placed on both the conceptual relationships between these different learning problems as well as the statistical models and computational methods used to employ those models.
|Spring 2020||Jie Hou||MWF 10:00am-10:50am|
|Spring 2019||Kevin Scannell||MWF 10:00am-10:50am|
|Spring 2018||Kevin Scannell||MWF 2:10pm-3:00pm|
|Fall 2015||Kevin Scannell||TR 9:30am-10:45am|