Modeling and Learning in Data Science credit:

4 Hours

Introduction to the use of classical approaches in data modeling and machine learning in the context of solving data-centric problems. A broad coverage of fundamental models is presented, including linear models, unsupervised learning, supervised learning, and deep learning. A significant emphasis is placed on the application of the models in Python and the interpretability of the results. Prerequisite: STAT 207 ; one of MATH 225 , MATH 227 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 .