M 368 - Predictive Modeling for Data Science

3 credit(s)
An introduction to predictive modeling and modern machine learning in data science. The main topics for the course include: Data visualization and transformation, linear regression, classification and logistic regression, unsupervised learning and k-means clustering, techniques for learning from data, assessing and comparing models, cross-validation, model selection and regularization, communicating with data. Emphasis is placed on statistical computing in a high-level language.
Prerequisite(s): M 260.

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