Essential foundations of machine learning; Instance-based learning; supervised learning (linear regression, logistic regression, support vector machines, decision tree, ensemble learning, neural networks, and generative classifiers); unsupervised learning (clustering, EM, mixture models, dimensionality reduction); Applications of Machine learning to real world problems.

Pre-Requisites: (COE 292 Or ICS 381*) And (STAT 319 Or STAT 201) And (MATH 208 Or ICS 254 Or MATH 202 Or MATH 260)

* indicates Co-Requisities