Machine Learning

Catalog description

Hands-on approach to methods of machine learning for data science, including support vector machines, dimensionality reduction and principal component analysis, unsupervised learning with K-means clustering, and artificial neural networks. Methods of model selection and evaluation. Applications are explored, some of which will be implemented using industry-standard libraries. Prerequisites: MAT-280 and CIS-172