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: (COE292 Or ICS381*) And STAT319 And (MATH208 Or ICS254 Or MATH202 Or MATH260)

Co-requisites: ICS 381