Students are introduced to machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Students learn core topics of machine learning, with a focus on applying existing tools and libraries of machine learning code to problems. Students explore practical considerations, such as preparation and manipulation of data, relevant theory and concepts key to understanding the capabilities and limitations of machine learning. Students are introduced to a number of the main machine learning methods such as preparation and manipulation of data, supervised (classification) and unsupervised (clustering) technique. Students learn to apply and write python code to carry out an analysis. Prerequisite: ADSC 3710 (min. grade C)
Students are introduced to machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Students learn core topics of machine learning, with a focus on applying existing tools and libraries of machine learning code to problems. Students explore practical considerations, such as preparation and manipulation of data, relevant theory and concepts key to understanding the capabilities and limitations of machine learning. Students are introduced to a number of the main machine learning methods such as preparation and manipulation of data, supervised (classification) and unsupervised (clustering) technique. Students learn to apply and write python code to carry out an analysis. Prerequisite: ADSC 3710 (min. grade C)
