Classical Machine Learning refers to well established techniques by which one makes inferences from data. This course will introduce a systematic approach (the “Recipe for Machine Learning”) and tools with which to accomplish this task. In addition to the typical models and algorithms taught (e.g., Linear and Logistic Regression) this course emphasizes the whole life cycle of the process, from data set acquisition and cleaning to analysis of errors, all in the service of an iterative process for improving inference.
Our belief is that Machine Learning is an experimental process and thus, most learning will be achieved by “doing”. We will jump-start your experimentation: Engineering first, then math. Early lectures will be a "sprint" to get you programming and experimenting. We will subsequently revisit topics on a greater mathematical basis.
